scikit-learn:3. Model selection and evaluation
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參考:http://scikit-learn.org/stable/model_selection.html
有待翻译,敬请期待:
- 3.1. Cross-validation: evaluating estimator performance
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翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47099275
- 3.2. Grid Search: Searching for estimator parameters
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翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47100091
- 3.2.1. Exhaustive Grid Search
- 3.2.2. Randomized Parameter Optimization
- 3.2.3. Tips for parameter search
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3.2.4. Alternatives
to brute force parameter search
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3.2.4.1. Model specific cross-validation
- 3.2.4.1.1. sklearn.linear_model.ElasticNetCV
- 3.2.4.1.2. sklearn.linear_model.LarsCV
- 3.2.4.1.3. sklearn.linear_model.LassoCV
- 3.2.4.1.4. sklearn.linear_model.LassoLarsCV
- 3.2.4.1.5. sklearn.linear_model.LogisticRegressionCV
- 3.2.4.1.6. sklearn.linear_model.MultiTaskElasticNetCV
- 3.2.4.1.7. sklearn.linear_model.MultiTaskLassoCV
- 3.2.4.1.8. sklearn.linear_model.OrthogonalMatchingPursuitCV
- 3.2.4.1.9. sklearn.linear_model.RidgeCV
- 3.2.4.1.10. sklearn.linear_model.RidgeClassifierCV
- 3.2.4.2. Information Criterion
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3.2.4.3. Out of Bag Estimates
- 3.2.4.3.1. sklearn.ensemble.RandomForestClassifier
- 3.2.4.3.2. sklearn.ensemble.RandomForestRegressor
- 3.2.4.3.3. sklearn.ensemble.ExtraTreesClassifier
- 3.2.4.3.4. sklearn.ensemble.ExtraTreesRegressor
- 3.2.4.3.5. sklearn.ensemble.GradientBoostingClassifier
- 3.2.4.3.6. sklearn.ensemble.GradientBoostingRegressor
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3.2.4.1. Model specific cross-validation
- 3.3. Model evaluation: quantifying the quality of predictions
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翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47121611
- 3.3.1. The scoring parameter: defining model evaluation rules
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3.3.2. Classification metrics
- 3.3.2.1. From binary to multiclass and multilabel
- 3.3.2.2. Accuracy score
- 3.3.2.3. Confusion matrix
- 3.3.2.4. Classification report
- 3.3.2.5. Hamming loss
- 3.3.2.6. Jaccard similarity coefficient score
- 3.3.2.7. Precision, recall and F-measures
- 3.3.2.8. Hinge loss
- 3.3.2.9. Log loss
- 3.3.2.10. Matthews correlation coefficient
- 3.3.2.11. Receiver operating characteristic (ROC)
- 3.3.2.12. Zero one loss
- 3.3.3. Multilabel ranking metrics
- 3.3.4. Regression metrics
- 3.3.5. Clustering metrics
- 3.3.6. Dummy estimators
- 3.4. Model persistence
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翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47143539
- 3.5. Validation curves: plotting scores to evaluate models
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翻译文章參考:http://blog.csdn.net/mmc2015/article/details/47144197
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