scikit-learn:3. Model selection and evaluation
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参考:http://scikit-learn.org/stable/model_selection.html
有待翻译,敬请期待:
- 3.1. Cross-validation: evaluating estimator performance
- 翻译文章参考:http://blog.csdn.net/mmc2015/article/details/47099275
- 3.1.1. Computing cross-validated metrics
- 3.1.1.1. Obtaining predictions by cross-validation
- 3.1.2. Cross validation iterators
- 3.1.2.1. K-fold
- 3.1.2.2. Stratified k-fold
- 3.1.2.3. Leave-One-Out - LOO
- 3.1.2.4. Leave-P-Out - LPO
- 3.1.2.5. Leave-One-Label-Out - LOLO
- 3.1.2.6. Leave-P-Label-Out
- 3.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split
- 3.1.2.8. Predefined Fold-Splits / Validation-Sets
- 3.1.2.9. See also
- 3.1.3. A note on shuffling
- 3.1.4. Cross validation and model selection
- 3.1.1. Computing cross-validated metrics
- 3.2. Grid Search: Searching for estimator parameters
- 翻译文章参考: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
- 3.2.3.1. Specifying an objective metric
- 3.2.3.2. Composite estimators and parameter spaces
- 3.2.3.3. Model selection: development and evaluation
- 3.2.3.4. Parallelism
- 3.2.3.5. Robustness to failure
- 3.2.4. Alternatives to brute force parameter search
- 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
- 3.2.4.1.3.1. Examples using sklearn.linear_model.LassoCV
- 3.2.4.1.4. sklearn.linear_model.LassoLarsCV
- 3.2.4.1.4.1. Examples using 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
- 3.2.4.1.8.1. Examples using sklearn.linear_model.OrthogonalMatchingPursuitCV
- 3.2.4.1.9. sklearn.linear_model.RidgeCV
- 3.2.4.1.9.1. Examples using sklearn.linear_model.RidgeCV
- 3.2.4.1.10. sklearn.linear_model.RidgeClassifierCV
- 3.2.4.2. Information Criterion
- 3.2.4.2.1. sklearn.linear_model.LassoLarsIC
- 3.2.4.2.1.1. Examples using sklearn.linear_model.LassoLarsIC
- 3.2.4.2.1. sklearn.linear_model.LassoLarsIC
- 3.2.4.3. Out of Bag Estimates
- 3.2.4.3.1. sklearn.ensemble.RandomForestClassifier
- 3.2.4.3.1.1. Examples using sklearn.ensemble.RandomForestClassifier
- 3.2.4.3.2. sklearn.ensemble.RandomForestRegressor
- 3.2.4.3.2.1. Examples using sklearn.ensemble.RandomForestRegressor
- 3.2.4.3.3. sklearn.ensemble.ExtraTreesClassifier
- 3.2.4.3.3.1. Examples using sklearn.ensemble.ExtraTreesClassifier
- 3.2.4.3.4. sklearn.ensemble.ExtraTreesRegressor
- 3.2.4.3.4.1. Examples using sklearn.ensemble.ExtraTreesRegressor
- 3.2.4.3.5. sklearn.ensemble.GradientBoostingClassifier
- 3.2.4.3.5.1. Examples using sklearn.ensemble.GradientBoostingClassifier
- 3.2.4.3.6. sklearn.ensemble.GradientBoostingRegressor
- 3.2.4.3.6.1. Examples using sklearn.ensemble.GradientBoostingRegressor
- 3.2.4.3.1. sklearn.ensemble.RandomForestClassifier
- 3.2.4.1. Model specific cross-validation
- 3.3. Model evaluation: quantifying the quality of predictions
- 翻译文章参考:http://blog.csdn.net/mmc2015/article/details/47121611
- 3.3.1. The scoring parameter: defining model evaluation rules
- 3.3.1.1. Common cases: predefined values
- 3.3.1.2. Defining your scoring strategy from metric functions
- 3.3.1.3. Implementing your own scoring object
- 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
- 3.3.2.7.1. Binary classification
- 3.3.2.7.2. Multiclass and multilabel classification
- 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
- 3.3.3.1. Coverage error
- 3.3.3.2. Label ranking average precision
- 3.3.4. Regression metrics
- 3.3.4.1. Explained variance score
- 3.3.4.2. Mean absolute error
- 3.3.4.3. Mean squared error
- 3.3.4.4. Median absolute error
- 3.3.4.5. R² score, the coefficient of determination
- 3.3.5. Clustering metrics
- 3.3.6. Dummy estimators
- 3.3.1. The scoring parameter: defining model evaluation rules
- 3.4. Model persistence
- 翻译文章参考:http://blog.csdn.net/mmc2015/article/details/47143539
- 3.4.1. Persistence example
- 3.4.2. Security & maintainability limitations
- 3.5. Validation curves: plotting scores to evaluate models
- 翻译文章参考:http://blog.csdn.net/mmc2015/article/details/47144197
- 3.5.1. Validation curve
- 3.5.2. Learning curve
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- scikit-learn:3. Model selection and evaluation
- scikit-learn:3. Model selection and evaluation
- Scikit-learn:模型评估Model evaluation
- 转:Scikit-learn:模型评估Model evaluation
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- scikit-learn:3.3. Model evaluation: quantifying the quality of predictions
- Scikit-learn:模型评估Model evaluation 之绘图
- 模型评估与选择(Model Evaluation and Selection)
- scikit-learn:3.4. Model persistence
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- 机器学习 Regularization and model selection
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- Using scikit-learn Pipelines and FeatureUnions
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