├── Chapter01 ├── .ipynb_checkpoints │ ├── Introducing Cross-Validation-checkpoint.ipynb │ ├── Loading the Iris Dataset-checkpoint.ipynb │ ├── Machine Learning Overview Classification vs. Regression-checkpoint.ipynb │ ├── Minimal Machine Learning Recipe SVM Classification-checkpoint.ipynb │ ├── Plotting with Numpy and Matplotlib-checkpoint.ipynb │ ├── Putting it All Together-checkpoint.ipynb │ ├── Viewing the Iris Dataset with Pandas-checkpoint.ipynb │ └── Viewing the Iris Dataset-checkpoint.ipynb ├── Introducing Cross-Validation.ipynb ├── Loading the Iris Dataset.ipynb ├── Machine Learning Overview Classification vs. Regression.ipynb ├── Minimal Machine Learning Recipe SVM Classification.ipynb ├── Numpy Basics.ipynb ├── Plotting with Numpy and Matplotlib.ipynb ├── Putting it All Together.ipynb ├── Viewing the Iris Dataset with Pandas.ipynb └── Viewing the Iris Dataset.ipynb ├── Chapter02 ├── .ipynb_checkpoints │ ├── A linear model in the presence of outliers-checkpoint.ipynb │ ├── Creating binary features through thresholding-checkpoint.ipynb │ ├── Creating sample data for toy analysis-checkpoint.ipynb │ ├── Imputing missing values through various strategies-checkpoint.ipynb │ ├── Putting it all together with Pipelines-checkpoint.ipynb │ ├── Scaling data to the standard normal-checkpoint.ipynb │ ├── Using Gaussian processes for regression-checkpoint.ipynb │ ├── Using stochastic gradient descent for regression-checkpoint.ipynb │ └── Working with categorical variables-checkpoint.ipynb ├── A linear model in the presence of outliers.ipynb ├── Creating binary features through thresholding.ipynb ├── Creating sample data for toy analysis.ipynb ├── Imputing missing values through various strategies.ipynb ├── Putting it all together with Pipelines.ipynb ├── Scaling data to the standard normal.ipynb ├── Using Gaussian processes for regression.ipynb ├── Using stochastic gradient descent for regression.ipynb └── Working with categorical variables.ipynb ├── Chapter03 ├── .ipynb_checkpoints │ ├── Decomposition to classify with DictionaryLearning-checkpoint.ipynb │ ├── Dimensionality Reduction with Manifolds tSNE-checkpoint.ipynb │ ├── Kernel PCA for nonlinear dimensionality reduction-checkpoint.ipynb │ ├── Pipelines Testing Methods to Reduce Dimensionality-checkpoint.ipynb │ ├── Reducing dimensionality with PCA-checkpoint.ipynb │ ├── Using factor analysis for decomposition-checkpoint.ipynb │ └── Using truncated SVD to reduce dimensionality-checkpoint.ipynb ├── Decomposition to classify with DictionaryLearning.ipynb ├── Dimensionality Reduction with Manifolds tSNE.ipynb ├── Kernel PCA for nonlinear dimensionality reduction.ipynb ├── Pipelines Testing Methods to Reduce Dimensionality.ipynb ├── Reducing dimensionality with PCA.ipynb ├── Using factor analysis for decomposition.ipynb └── Using truncated SVD to reduce dimensionality.ipynb ├── Chapter04 ├── .ipynb_checkpoints │ ├── Evaluating the linear regression model-checkpoint.ipynb │ ├── Fitting a line through data with Machine Learning-checkpoint.ipynb │ ├── Fitting a line through data-checkpoint.ipynb │ ├── Optimizing the ridge regression parameter-checkpoint.ipynb │ ├── Taking a more fundamental approach to regularization with LARS-checkpoint.ipynb │ ├── Using ridge regression to overcome linear regression's shortfalls-checkpoint.ipynb │ └── Using sparsity to regularize models-checkpoint.ipynb ├── Evaluating the linear regression model.ipynb ├── Fitting a line through data with Machine Learning.ipynb ├── Fitting a line through data.ipynb ├── Optimizing the ridge regression parameter.ipynb ├── Taking a more fundamental approach to regularization with LARS.ipynb ├── Using ridge regression to overcome linear regression's shortfalls.ipynb └── Using sparsity to regularize models.ipynb ├── Chapter05 ├── .ipynb_checkpoints │ ├── Examining Logistic Regression Errors with a confusion matrix-checkpoint.ipynb │ ├── Loading Data from the UCI Repository-checkpoint.ipynb │ ├── Machine Learning with Logistic Regression-checkpoint.ipynb │ ├── Plotting an ROC Curve without Context-checkpoint.ipynb │ ├── Putting it All Together UCI Breast Cancer Data Set-checkpoint.ipynb │ ├── ROC Analysis-checkpoint.ipynb │ ├── Varying the Classification Threshold in Logistic Regression-checkpoint.ipynb │ └── Viewing the Pima Indians Diabetes Dataset with Pandas-checkpoint.ipynb ├── Examining Logistic Regression Errors with a confusion matrix.ipynb ├── Loading Data from the UCI Repository.ipynb ├── Machine Learning with Logistic Regression.ipynb ├── Plotting an ROC Curve without Context.ipynb ├── Putting it All Together UCI Breast Cancer Data Set.ipynb ├── ROC Analysis.ipynb ├── Varying the Classification Threshold in Logistic Regression.ipynb └── Viewing the Pima Indians Diabetes Dataset with Pandas.ipynb ├── Chapter06 ├── .ipynb_checkpoints │ ├── Assessing cluster correctness-checkpoint.ipynb │ ├── Finding the closest object in the feature space-checkpoint.ipynb │ ├── Optimizing the number of centroids-checkpoint.ipynb │ ├── Probabilistic clustering with Gaussian Mixture Models-checkpoint.ipynb │ ├── Quantizing an image with KMeans clustering-checkpoint.ipynb │ ├── Using KMeans for outlier detection-checkpoint.ipynb │ ├── Using KMeans to cluster data-checkpoint.ipynb │ ├── Using MiniBatch KMeans to handle more data-checkpoint.ipynb │ └── Using k-NN for regression-checkpoint.ipynb ├── Assessing cluster correctness.ipynb ├── Finding the closest object in the feature space.ipynb ├── Optimizing the number of centroids.ipynb ├── Probabilistic clustering with Gaussian Mixture Models.ipynb ├── Quantizing an image with KMeans clustering.ipynb ├── Using KMeans for outlier detection.ipynb ├── Using KMeans to cluster data.ipynb ├── Using MiniBatch KMeans to handle more data.ipynb ├── Using k-NN for regression.ipynb └── headshot.jpg ├── Chapter07 ├── .ipynb_checkpoints │ ├── Balanced cross-validation-checkpoint.ipynb │ ├── Classification Metrics-checkpoint.ipynb │ ├── Clustering Metrics-checkpoint.ipynb │ ├── Cross-validation with ShuffleSplit-checkpoint.ipynb │ ├── Feature selection on L1 norms-checkpoint.ipynb │ ├── Feature selection-checkpoint.ipynb │ ├── Grid Search with scikit-learn-checkpoint.ipynb │ ├── K-fold cross validation-checkpoint.ipynb │ ├── Persisting models with joblib or pickle-checkpoint.ipynb │ ├── Randomized Search with scikit-learn-checkpoint.ipynb │ ├── Regression Metrics-checkpoint.ipynb │ ├── Select a Model with Cross Validation-checkpoint.ipynb │ ├── Time-Series Cross Validation-checkpoint.ipynb │ └── Using dummy estimators to compare results-checkpoint.ipynb ├── Balanced cross-validation.ipynb ├── Classification Metrics.ipynb ├── Clustering Metrics.ipynb ├── Cross-validation with ShuffleSplit.ipynb ├── Feature selection on L1 norms.ipynb ├── Feature selection.ipynb ├── Grid Search with scikit-learn.ipynb ├── K-fold cross validation.ipynb ├── Persisting models with joblib or pickle.ipynb ├── Randomized Search with scikit-learn.ipynb ├── Regression Metrics.ipynb ├── Select a Model with Cross Validation.ipynb ├── Time-Series Cross Validation.ipynb ├── Using dummy estimators to compare results.ipynb ├── dtree.clf └── dtree.save ├── Chapter08 ├── .ipynb_checkpoints │ ├── Classifying data with a Linear Support Vector Machines-checkpoint.ipynb │ ├── Multiclass SVC Classifier-checkpoint.ipynb │ ├── Optimizing a Support Vector Machine-checkpoint.ipynb │ └── Support Vector Regression-checkpoint.ipynb ├── Classifying data with a Linear Support Vector Machines.ipynb ├── Multiclass SVC Classifier.ipynb ├── Optimizing a Support Vector Machine.ipynb └── Support Vector Regression.ipynb ├── Chapter09 ├── .ipynb_checkpoints │ ├── Bagging Regression with Nearest Neighbors-checkpoint.ipynb │ ├── Classifying documents with Naive Bayes-checkpoint.ipynb │ ├── Decision Trees for Regression-checkpoint.ipynb │ ├── Doing basic classifications with Decision Trees-checkpoint.ipynb │ ├── Random Forest Regression-checkpoint.ipynb │ ├── Reducing Overfitting with Cross-Validation-checkpoint.ipynb │ ├── Tuning Gradient Boosting Trees-checkpoint.ipynb │ ├── Tuning a Decision Tree-checkpoint.ipynb │ ├── Tuning a Random Forest-checkpoint.ipynb │ ├── Tuning an Ada Boost Regressor-checkpoint.ipynb │ ├── Using LDA for classification-checkpoint.ipynb │ ├── Using Stochastic Gradient Descent for classification-checkpoint.ipynb │ ├── Visualize a Decision Tree with pydot-checkpoint.ipynb │ ├── Working with QDA - a nonlinear LDA-checkpoint.ipynb │ └── Writing A Stacking Agreggator with Scikit-Learn-checkpoint.ipynb ├── Bagging Regression with Nearest Neighbors.ipynb ├── Decision Trees for Regression.ipynb ├── Doing basic classifications with Decision Trees.ipynb ├── Random Forest Regression.ipynb ├── Reducing Overfitting with Cross-Validation.ipynb ├── Tuning Gradient Boosting Trees.ipynb ├── Tuning a Decision Tree.ipynb ├── Tuning a Random Forest.ipynb ├── Tuning an Ada Boost Regressor.ipynb ├── Visualize a Decision Tree with pydot.ipynb ├── Writing A Stacking Agreggator with Scikit-Learn Extra.ipynb ├── Writing A Stacking Agreggator with Scikit-Learn.ipynb └── Writing+A+Stacking+Agreggator+with+Scikit-Learn+Extra(2).html ├── Chapter10 ├── Classifying documents with Naive Bayes.ipynb ├── Using LDA for classification.ipynb ├── Using Stochastic Gradient Descent for classification.ipynb └── Working with QDA - a nonlinear LDA.ipynb ├── Chapter11 ├── .ipynb_checkpoints │ ├── Neural Network Multi-Layer Perceptron-checkpoint.ipynb │ ├── Perceptron Classifier-checkpoint.ipynb │ └── Stacking with a Neural Network-checkpoint.ipynb ├── Neural Network Multi-Layer Perceptron.ipynb ├── Perceptron Classifier.ipynb ├── Stacking with a Neural Network.ipynb ├── bag_gbm.save ├── grad_boost.save └── nn_best.save ├── Chapter12 ├── .ipynb_checkpoints │ └── Create a Simple Estimator-checkpoint.ipynb ├── Create a Simple Estimator.ipynb └── rc_inst.save ├── LICENSE └── README.md /Chapter01/.ipynb_checkpoints/Introducing Cross-Validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Introducing Cross-Validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Loading the Iris Dataset-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Loading the Iris Dataset-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Machine Learning Overview Classification vs. Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Machine Learning Overview Classification vs. Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Minimal Machine Learning Recipe SVM Classification-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Minimal Machine Learning Recipe SVM Classification-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Plotting with Numpy and Matplotlib-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Plotting with Numpy and Matplotlib-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Putting it All Together-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Putting it All Together-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Viewing the Iris Dataset with Pandas-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Viewing the Iris Dataset with Pandas-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/.ipynb_checkpoints/Viewing the Iris Dataset-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/.ipynb_checkpoints/Viewing the Iris Dataset-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter01/Introducing Cross-Validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Introducing Cross-Validation.ipynb -------------------------------------------------------------------------------- /Chapter01/Loading the Iris Dataset.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Loading the Iris Dataset.ipynb -------------------------------------------------------------------------------- /Chapter01/Machine Learning Overview Classification vs. Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Machine Learning Overview Classification vs. Regression.ipynb -------------------------------------------------------------------------------- /Chapter01/Minimal Machine Learning Recipe SVM Classification.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Minimal Machine Learning Recipe SVM Classification.ipynb -------------------------------------------------------------------------------- /Chapter01/Numpy Basics.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Numpy Basics.ipynb -------------------------------------------------------------------------------- /Chapter01/Plotting with Numpy and Matplotlib.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Plotting with Numpy and Matplotlib.ipynb -------------------------------------------------------------------------------- /Chapter01/Putting it All Together.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Putting it All Together.ipynb -------------------------------------------------------------------------------- /Chapter01/Viewing the Iris Dataset with Pandas.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Viewing the Iris Dataset with Pandas.ipynb -------------------------------------------------------------------------------- /Chapter01/Viewing the Iris Dataset.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter01/Viewing the Iris Dataset.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/A linear model in the presence of outliers-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/A linear model in the presence of outliers-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Creating binary features through thresholding-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Creating binary features through thresholding-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Creating sample data for toy analysis-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Creating sample data for toy analysis-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Imputing missing values through various strategies-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Imputing missing values through various strategies-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Putting it all together with Pipelines-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Putting it all together with Pipelines-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Scaling data to the standard normal-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Scaling data to the standard normal-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Using Gaussian processes for regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Using Gaussian processes for regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Using stochastic gradient descent for regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Using stochastic gradient descent for regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Working with categorical variables-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/.ipynb_checkpoints/Working with categorical variables-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter02/A linear model in the presence of outliers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/A linear model in the presence of outliers.ipynb -------------------------------------------------------------------------------- /Chapter02/Creating binary features through thresholding.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Creating binary features through thresholding.ipynb -------------------------------------------------------------------------------- /Chapter02/Creating sample data for toy analysis.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Creating sample data for toy analysis.ipynb -------------------------------------------------------------------------------- /Chapter02/Imputing missing values through various strategies.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Imputing missing values through various strategies.ipynb -------------------------------------------------------------------------------- /Chapter02/Putting it all together with Pipelines.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Putting it all together with Pipelines.ipynb -------------------------------------------------------------------------------- /Chapter02/Scaling data to the standard normal.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Scaling data to the standard normal.ipynb -------------------------------------------------------------------------------- /Chapter02/Using Gaussian processes for regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Using Gaussian processes for regression.ipynb -------------------------------------------------------------------------------- /Chapter02/Using stochastic gradient descent for regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Using stochastic gradient descent for regression.ipynb -------------------------------------------------------------------------------- /Chapter02/Working with categorical variables.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter02/Working with categorical variables.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Decomposition to classify with DictionaryLearning-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Decomposition to classify with DictionaryLearning-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Dimensionality Reduction with Manifolds tSNE-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Dimensionality Reduction with Manifolds tSNE-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Kernel PCA for nonlinear dimensionality reduction-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Kernel PCA for nonlinear dimensionality reduction-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Pipelines Testing Methods to Reduce Dimensionality-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Pipelines Testing Methods to Reduce Dimensionality-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Reducing dimensionality with PCA-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Reducing dimensionality with PCA-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Using factor analysis for decomposition-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Using factor analysis for decomposition-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Using truncated SVD to reduce dimensionality-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/.ipynb_checkpoints/Using truncated SVD to reduce dimensionality-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter03/Decomposition to classify with DictionaryLearning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Decomposition to classify with DictionaryLearning.ipynb -------------------------------------------------------------------------------- /Chapter03/Dimensionality Reduction with Manifolds tSNE.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Dimensionality Reduction with Manifolds tSNE.ipynb -------------------------------------------------------------------------------- /Chapter03/Kernel PCA for nonlinear dimensionality reduction.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Kernel PCA for nonlinear dimensionality reduction.ipynb -------------------------------------------------------------------------------- /Chapter03/Pipelines Testing Methods to Reduce Dimensionality.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Pipelines Testing Methods to Reduce Dimensionality.ipynb -------------------------------------------------------------------------------- /Chapter03/Reducing dimensionality with PCA.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Reducing dimensionality with PCA.ipynb -------------------------------------------------------------------------------- /Chapter03/Using factor analysis for decomposition.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Using factor analysis for decomposition.ipynb -------------------------------------------------------------------------------- /Chapter03/Using truncated SVD to reduce dimensionality.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter03/Using truncated SVD to reduce dimensionality.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Evaluating the linear regression model-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Evaluating the linear regression model-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Fitting a line through data with Machine Learning-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Fitting a line through data with Machine Learning-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Fitting a line through data-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Fitting a line through data-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Optimizing the ridge regression parameter-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Optimizing the ridge regression parameter-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Taking a more fundamental approach to regularization with LARS-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Taking a more fundamental approach to regularization with LARS-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Using ridge regression to overcome linear regression's shortfalls-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Using ridge regression to overcome linear regression's shortfalls-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Using sparsity to regularize models-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/.ipynb_checkpoints/Using sparsity to regularize models-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter04/Evaluating the linear regression model.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Evaluating the linear regression model.ipynb -------------------------------------------------------------------------------- /Chapter04/Fitting a line through data with Machine Learning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Fitting a line through data with Machine Learning.ipynb -------------------------------------------------------------------------------- /Chapter04/Fitting a line through data.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Fitting a line through data.ipynb -------------------------------------------------------------------------------- /Chapter04/Optimizing the ridge regression parameter.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Optimizing the ridge regression parameter.ipynb -------------------------------------------------------------------------------- /Chapter04/Taking a more fundamental approach to regularization with LARS.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Taking a more fundamental approach to regularization with LARS.ipynb -------------------------------------------------------------------------------- /Chapter04/Using ridge regression to overcome linear regression's shortfalls.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Using ridge regression to overcome linear regression's shortfalls.ipynb -------------------------------------------------------------------------------- /Chapter04/Using sparsity to regularize models.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter04/Using sparsity to regularize models.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Examining Logistic Regression Errors with a confusion matrix-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Examining Logistic Regression Errors with a confusion matrix-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Loading Data from the UCI Repository-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Loading Data from the UCI Repository-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Machine Learning with Logistic Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Machine Learning with Logistic Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Plotting an ROC Curve without Context-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Plotting an ROC Curve without Context-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Putting it All Together UCI Breast Cancer Data Set-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Putting it All Together UCI Breast Cancer Data Set-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/ROC Analysis-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/ROC Analysis-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Varying the Classification Threshold in Logistic Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Varying the Classification Threshold in Logistic Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/.ipynb_checkpoints/Viewing the Pima Indians Diabetes Dataset with Pandas-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/.ipynb_checkpoints/Viewing the Pima Indians Diabetes Dataset with Pandas-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter05/Examining Logistic Regression Errors with a confusion matrix.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Examining Logistic Regression Errors with a confusion matrix.ipynb -------------------------------------------------------------------------------- /Chapter05/Loading Data from the UCI Repository.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Loading Data from the UCI Repository.ipynb -------------------------------------------------------------------------------- /Chapter05/Machine Learning with Logistic Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Machine Learning with Logistic Regression.ipynb -------------------------------------------------------------------------------- /Chapter05/Plotting an ROC Curve without Context.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Plotting an ROC Curve without Context.ipynb -------------------------------------------------------------------------------- /Chapter05/Putting it All Together UCI Breast Cancer Data Set.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Putting it All Together UCI Breast Cancer Data Set.ipynb -------------------------------------------------------------------------------- /Chapter05/ROC Analysis.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/ROC Analysis.ipynb -------------------------------------------------------------------------------- /Chapter05/Varying the Classification Threshold in Logistic Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Varying the Classification Threshold in Logistic Regression.ipynb -------------------------------------------------------------------------------- /Chapter05/Viewing the Pima Indians Diabetes Dataset with Pandas.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter05/Viewing the Pima Indians Diabetes Dataset with Pandas.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Assessing cluster correctness-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Assessing cluster correctness-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Finding the closest object in the feature space-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Finding the closest object in the feature space-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Optimizing the number of centroids-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Optimizing the number of centroids-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Probabilistic clustering with Gaussian Mixture Models-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Probabilistic clustering with Gaussian Mixture Models-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Quantizing an image with KMeans clustering-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Quantizing an image with KMeans clustering-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Using KMeans for outlier detection-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Using KMeans for outlier detection-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Using KMeans to cluster data-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Using KMeans to cluster data-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Using MiniBatch KMeans to handle more data-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Using MiniBatch KMeans to handle more data-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Using k-NN for regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/.ipynb_checkpoints/Using k-NN for regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter06/Assessing cluster correctness.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Assessing cluster correctness.ipynb -------------------------------------------------------------------------------- /Chapter06/Finding the closest object in the feature space.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Finding the closest object in the feature space.ipynb -------------------------------------------------------------------------------- /Chapter06/Optimizing the number of centroids.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Optimizing the number of centroids.ipynb -------------------------------------------------------------------------------- /Chapter06/Probabilistic clustering with Gaussian Mixture Models.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Probabilistic clustering with Gaussian Mixture Models.ipynb -------------------------------------------------------------------------------- /Chapter06/Quantizing an image with KMeans clustering.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Quantizing an image with KMeans clustering.ipynb -------------------------------------------------------------------------------- /Chapter06/Using KMeans for outlier detection.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Using KMeans for outlier detection.ipynb -------------------------------------------------------------------------------- /Chapter06/Using KMeans to cluster data.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Using KMeans to cluster data.ipynb -------------------------------------------------------------------------------- /Chapter06/Using MiniBatch KMeans to handle more data.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Using MiniBatch KMeans to handle more data.ipynb -------------------------------------------------------------------------------- /Chapter06/Using k-NN for regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/Using k-NN for regression.ipynb -------------------------------------------------------------------------------- /Chapter06/headshot.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter06/headshot.jpg -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Balanced cross-validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Balanced cross-validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Classification Metrics-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Classification Metrics-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Clustering Metrics-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Clustering Metrics-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Cross-validation with ShuffleSplit-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Cross-validation with ShuffleSplit-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Feature selection on L1 norms-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Feature selection on L1 norms-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Feature selection-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Feature selection-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Grid Search with scikit-learn-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Grid Search with scikit-learn-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/K-fold cross validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/K-fold cross validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Persisting models with joblib or pickle-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Persisting models with joblib or pickle-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Randomized Search with scikit-learn-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Randomized Search with scikit-learn-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Regression Metrics-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Regression Metrics-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Select a Model with Cross Validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Select a Model with Cross Validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Time-Series Cross Validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Time-Series Cross Validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Using dummy estimators to compare results-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/.ipynb_checkpoints/Using dummy estimators to compare results-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter07/Balanced cross-validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Balanced cross-validation.ipynb -------------------------------------------------------------------------------- /Chapter07/Classification Metrics.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Classification Metrics.ipynb -------------------------------------------------------------------------------- /Chapter07/Clustering Metrics.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Clustering Metrics.ipynb -------------------------------------------------------------------------------- /Chapter07/Cross-validation with ShuffleSplit.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Cross-validation with ShuffleSplit.ipynb -------------------------------------------------------------------------------- /Chapter07/Feature selection on L1 norms.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Feature selection on L1 norms.ipynb -------------------------------------------------------------------------------- /Chapter07/Feature selection.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Feature selection.ipynb -------------------------------------------------------------------------------- /Chapter07/Grid Search with scikit-learn.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Grid Search with scikit-learn.ipynb -------------------------------------------------------------------------------- /Chapter07/K-fold cross validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/K-fold cross validation.ipynb -------------------------------------------------------------------------------- /Chapter07/Persisting models with joblib or pickle.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Persisting models with joblib or pickle.ipynb -------------------------------------------------------------------------------- /Chapter07/Randomized Search with scikit-learn.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Randomized Search with scikit-learn.ipynb -------------------------------------------------------------------------------- /Chapter07/Regression Metrics.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Regression Metrics.ipynb -------------------------------------------------------------------------------- /Chapter07/Select a Model with Cross Validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Select a Model with Cross Validation.ipynb -------------------------------------------------------------------------------- /Chapter07/Time-Series Cross Validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Time-Series Cross Validation.ipynb -------------------------------------------------------------------------------- /Chapter07/Using dummy estimators to compare results.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/Using dummy estimators to compare results.ipynb -------------------------------------------------------------------------------- /Chapter07/dtree.clf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/dtree.clf -------------------------------------------------------------------------------- /Chapter07/dtree.save: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter07/dtree.save -------------------------------------------------------------------------------- /Chapter08/.ipynb_checkpoints/Classifying data with a Linear Support Vector Machines-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/.ipynb_checkpoints/Classifying data with a Linear Support Vector Machines-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter08/.ipynb_checkpoints/Multiclass SVC Classifier-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/.ipynb_checkpoints/Multiclass SVC Classifier-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter08/.ipynb_checkpoints/Optimizing a Support Vector Machine-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/.ipynb_checkpoints/Optimizing a Support Vector Machine-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter08/.ipynb_checkpoints/Support Vector Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/.ipynb_checkpoints/Support Vector Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter08/Classifying data with a Linear Support Vector Machines.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/Classifying data with a Linear Support Vector Machines.ipynb -------------------------------------------------------------------------------- /Chapter08/Multiclass SVC Classifier.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/Multiclass SVC Classifier.ipynb -------------------------------------------------------------------------------- /Chapter08/Optimizing a Support Vector Machine.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/Optimizing a Support Vector Machine.ipynb -------------------------------------------------------------------------------- /Chapter08/Support Vector Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter08/Support Vector Regression.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Bagging Regression with Nearest Neighbors-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Bagging Regression with Nearest Neighbors-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Classifying documents with Naive Bayes-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Classifying documents with Naive Bayes-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Decision Trees for Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Decision Trees for Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Doing basic classifications with Decision Trees-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Doing basic classifications with Decision Trees-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Random Forest Regression-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Random Forest Regression-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Reducing Overfitting with Cross-Validation-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Reducing Overfitting with Cross-Validation-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Tuning Gradient Boosting Trees-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Tuning Gradient Boosting Trees-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Tuning a Decision Tree-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Tuning a Decision Tree-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Tuning a Random Forest-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Tuning a Random Forest-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Tuning an Ada Boost Regressor-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Tuning an Ada Boost Regressor-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Using LDA for classification-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Using LDA for classification-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Using Stochastic Gradient Descent for classification-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Using Stochastic Gradient Descent for classification-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Visualize a Decision Tree with pydot-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Visualize a Decision Tree with pydot-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Working with QDA - a nonlinear LDA-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Working with QDA - a nonlinear LDA-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Writing A Stacking Agreggator with Scikit-Learn-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/.ipynb_checkpoints/Writing A Stacking Agreggator with Scikit-Learn-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter09/Bagging Regression with Nearest Neighbors.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Bagging Regression with Nearest Neighbors.ipynb -------------------------------------------------------------------------------- /Chapter09/Decision Trees for Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Decision Trees for Regression.ipynb -------------------------------------------------------------------------------- /Chapter09/Doing basic classifications with Decision Trees.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Doing basic classifications with Decision Trees.ipynb -------------------------------------------------------------------------------- /Chapter09/Random Forest Regression.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Random Forest Regression.ipynb -------------------------------------------------------------------------------- /Chapter09/Reducing Overfitting with Cross-Validation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Reducing Overfitting with Cross-Validation.ipynb -------------------------------------------------------------------------------- /Chapter09/Tuning Gradient Boosting Trees.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Tuning Gradient Boosting Trees.ipynb -------------------------------------------------------------------------------- /Chapter09/Tuning a Decision Tree.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Tuning a Decision Tree.ipynb -------------------------------------------------------------------------------- /Chapter09/Tuning a Random Forest.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Tuning a Random Forest.ipynb -------------------------------------------------------------------------------- /Chapter09/Tuning an Ada Boost Regressor.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Tuning an Ada Boost Regressor.ipynb -------------------------------------------------------------------------------- /Chapter09/Visualize a Decision Tree with pydot.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Visualize a Decision Tree with pydot.ipynb -------------------------------------------------------------------------------- /Chapter09/Writing A Stacking Agreggator with Scikit-Learn Extra.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Writing A Stacking Agreggator with Scikit-Learn Extra.ipynb -------------------------------------------------------------------------------- /Chapter09/Writing A Stacking Agreggator with Scikit-Learn.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Writing A Stacking Agreggator with Scikit-Learn.ipynb -------------------------------------------------------------------------------- /Chapter09/Writing+A+Stacking+Agreggator+with+Scikit-Learn+Extra(2).html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter09/Writing+A+Stacking+Agreggator+with+Scikit-Learn+Extra(2).html -------------------------------------------------------------------------------- /Chapter10/Classifying documents with Naive Bayes.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter10/Classifying documents with Naive Bayes.ipynb -------------------------------------------------------------------------------- /Chapter10/Using LDA for classification.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter10/Using LDA for classification.ipynb -------------------------------------------------------------------------------- /Chapter10/Using Stochastic Gradient Descent for classification.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter10/Using Stochastic Gradient Descent for classification.ipynb -------------------------------------------------------------------------------- /Chapter10/Working with QDA - a nonlinear LDA.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter10/Working with QDA - a nonlinear LDA.ipynb -------------------------------------------------------------------------------- /Chapter11/.ipynb_checkpoints/Neural Network Multi-Layer Perceptron-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/.ipynb_checkpoints/Neural Network Multi-Layer Perceptron-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter11/.ipynb_checkpoints/Perceptron Classifier-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/.ipynb_checkpoints/Perceptron Classifier-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter11/.ipynb_checkpoints/Stacking with a Neural Network-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/.ipynb_checkpoints/Stacking with a Neural Network-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter11/Neural Network Multi-Layer Perceptron.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/Neural Network Multi-Layer Perceptron.ipynb -------------------------------------------------------------------------------- /Chapter11/Perceptron Classifier.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/Perceptron Classifier.ipynb -------------------------------------------------------------------------------- /Chapter11/Stacking with a Neural Network.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/Stacking with a Neural Network.ipynb -------------------------------------------------------------------------------- /Chapter11/bag_gbm.save: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/bag_gbm.save -------------------------------------------------------------------------------- /Chapter11/grad_boost.save: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/grad_boost.save -------------------------------------------------------------------------------- /Chapter11/nn_best.save: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter11/nn_best.save -------------------------------------------------------------------------------- /Chapter12/.ipynb_checkpoints/Create a Simple Estimator-checkpoint.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter12/.ipynb_checkpoints/Create a Simple Estimator-checkpoint.ipynb -------------------------------------------------------------------------------- /Chapter12/Create a Simple Estimator.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter12/Create a Simple Estimator.ipynb -------------------------------------------------------------------------------- /Chapter12/rc_inst.save: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/Chapter12/rc_inst.save -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/LICENSE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/scikit-learn-Cookbook-Second-Edition/HEAD/README.md --------------------------------------------------------------------------------