Search

Searching

• 1.1. Linear Models

  ...cases of regression without penalization. The statsmodels package natively supports this. Within sklearn, one could use bootstrapping instead as well. LogisticRegressionCV implements Logistic Regression with built-in cross-validation suppo...

• 1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking

  .... 1.11.1.2.6. Subsampling [Friedman2002] proposed stochastic gradient boosting, which combines gradient boosting with bootstrap averaging (bagging). At each iteration the base classifier is trained on a fraction subsample of the available...

• 3.1. Cross-validation: evaluating estimator performance
• AdaBoostRegressor

  ...r at each boosting iteration. Thus, it is only used when estimator exposes a random_state. In addition, it controls the bootstrap of the weights used to train the estimator at each boosting iteration. Pass an int for reproducible output acr...

• BaggingClassifier
• BaggingRegressor

  ...ressor class sklearn.ensemble.BaggingRegressor(estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0)[...

• Class Likelihood Ratios to measure classification performance
• compute_sample_weight
• ExtraTreesClassifier
• ExtraTreesRegressor
• Gradient Boosting Out-of-Bag estimates
• Imputing missing values with variants of IterativeImputer
• IsolationForest
• Older Versions
• OOB Errors for Random Forests
• RandomForestClassifier
• RandomForestRegressor
• Release Highlights for scikit-learn 0.24
• resample