├── README.md
├── 1_regression
    ├── 1_1_random_forest_regressor.py
    └── 2_1_iris_dataset.py
└── LICENSE


/README.md:
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1 | # sklearn-playground


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/1_regression/1_1_random_forest_regressor.py:
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 1 | import numpy as np
 2 | import matplotlib.pyplot as plt
 3 | from sklearn.linear_model import LinearRegression
 4 | import seaborn; seaborn.set()
 5 | 
 6 | model = LinearRegression(normalize=True)
 7 | x = np.arange(10)
 8 | y = 2 * x + 1
 9 | 
10 | np.random.seed(0)
11 | X = np.random.random(size=(20, 1))
12 | y = 3 * X.squeeze() + 2 + np.random.randn(20)
13 | 
14 | plt.plot(X.squeeze(), y, 'o');
15 | 
16 | from sklearn.ensemble import RandomForestRegressor
17 | model = RandomForestRegressor()
18 | model.fit(X, y)
19 | 
20 | # Plot the data and the model prediction
21 | X_fit = np.linspace(0, 1, 100)[:, np.newaxis]
22 | y_fit = model.predict(X_fit)
23 | 
24 | plt.plot(X.squeeze(), y, 'o')
25 | plt.plot(X_fit.squeeze(), y_fit);
26 | plt.show()


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/1_regression/2_1_iris_dataset.py:
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 1 | from sklearn.datasets import load_iris
 2 | iris = load_iris()
 3 | 
 4 | iris.keys()
 5 | print(iris.keys())
 6 | 
 7 | n_samples, n_features = iris.data.shape
 8 | print((n_samples, n_features))
 9 | print(iris.data[0])
10 | 
11 | print(iris.data.shape)
12 | print(iris.target.shape)	
13 | 
14 | print(iris.target)
15 | print(iris.target_names)
16 | 
17 | import numpy as np
18 | import matplotlib.pyplot as plt
19 | 
20 | x_index = 0
21 | y_index = 1
22 | 
23 | # This formatter will label the colorbar with the correct target names.
24 | formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])
25 | 
26 | plt.scatter(iris.data[:, x_index], iris.data[:, y_index],
27 |             c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))
28 | plt.colorbar(ticks=[0, 1, 2], format=formatter)
29 | plt.clim(-0.5, 2.5)
30 | plt.xlabel(iris.feature_names[x_index])
31 | plt.ylabel(iris.feature_names[y_index]);
32 | 
33 | plt.show()


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 Andrew Stepin
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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