├── 1. Data Exploration ├── DEA.csv ├── ReadMe.md ├── 专题1：分类问题的数据探索(以Titanic数据集为例).ipynb ├── 专题2：回归问题中的相关系数矩阵与热力图(以Boston数据集为例).ipynb └── 专题3：对iris数据集进行数据探索.ipynb ├── 2. Data Preprocessing ├── ReadMe.md ├── 专题1：数据的标准化、归一化与正则化.ipynb ├── 专题2：One-Hot编码.ipynb ├── 专题3：缺失值处理.ipynb ├── 专题4：共线性问题.ipynb └── 专题5：非正态分布数据的处理.ipynb ├── 3. Feature Engineering ├── 3.1 Feature Selection │ ├── Feature Selection.ipynb │ ├── ReadMe.md │ ├── embedded.py │ ├── filter.py │ └── wrapper.py ├── 3.2 Feature Extraction │ ├── ReadMe.md │ ├── cat_svd.jpg │ ├── pca_evd.py │ ├── pca_svd.py │ ├── 运用TruncatedSVD进行图像处理.ipynb │ └── 验证：sklearn采用SVD实现PCA.ipynb ├── 3.3 Feature Construction │ ├── ReadMe.md │ ├── create_time_feature.py │ ├── high_categorical.py │ ├── 根据时间戳生成时间型索引&透视分析.ipynb │ ├── 生成哑变量.ipynb │ ├── 连续型特征的分箱处理.ipynb │ ├── 长尾数据的处理.ipynb │ └── 高基数类别特征的处理.ipynb └── ReadMe.md ├── 4. Classical Supervised Learning └── ReadMe.md ├── 5. Ensemble Learning ├── Boosting │ ├── GBDT-LR │ │ ├── GBDT系列与LR的融合&性能对比.ipynb │ │ ├── ReadMe.md │ │ ├── gbdt_lr.py │ │ ├── lightgbm_lr.py │ │ └── xgboost_lr.py │ ├── ReadMe.md │ ├── XGB自定义损失&可视化.ipynb │ ├── early_stopping_rounds.ipynb │ ├── gbdt_lr_contrast.jpg │ ├── xgb_custom_lossfunc.py │ ├── xgb_early_stopping.jpg │ ├── xgb_early_stopping.py │ └── xgb_loss.jpg ├── ReadMe.md └── Stacking │ ├── ReadMe.md │ ├── StackingModels_vs_Mlxtend.py │ └── stacking_models.py ├── 6. Cluster Analysis └── ReadMe.md ├── 7. Model Evaluation ├── Pics │ ├── ks curve.jpg │ ├── prc.jpg │ └── roc.jpg ├── ReadMe.md ├── ks_curve.py ├── ks_value.py ├── prc.py ├── roc.py ├── 交叉验证.ipynb ├── 分类与回归模型评估.ipynb └── 绘制KS、ROC、PRC曲线.ipynb ├── 8. Model Persistence ├── ReadMe.md ├── joblib.py └── pickle.py ├── 9. The Foundations of ML └── ReadMe.md └── README.md /1. 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