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mutual_info_score — scikit-learn 1.7.2 document...
Gallery examples: Adjustment for chance in clustering performance evaluationscikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html -
fetch_lfw_people — scikit-learn 1.7.2 documenta...
Gallery examples: Faces recognition example using eigenfaces and SVMsscikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html -
orthogonal_mp_gram — scikit-learn 1.7.2 documen...
Skip to main content Back to top Ctrl + K GitHub Choose version orthogonal_mp_gram # sklearn.linear_model. orthogonal...scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html -
add_dummy_feature — scikit-learn 1.7.2 document...
Skip to main content Back to top Ctrl + K GitHub Choose version add_dummy_feature # sklearn.preprocessing. add_dummy_...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.add_dummy_feature.html -
make_column_transformer — scikit-learn 1.7.2 do...
Gallery examples: Categorical Feature Support in Gradient Boosting Combine predictors using stacking Common pitfalls in the interpretation of coefficients of linear models Displaying estimators and...scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html -
enable_iterative_imputer — scikit-learn 1.7.2 d...
Enables IterativeImputer The API and results of this estimator might change without any deprecation cycle. Importing this file dynamically sets IterativeImputer as an attribute of the impute module:scikit-learn.org/stable/modules/generated/sklearn.experimental.enable_iterative_imputer.html -
sklearn.model_selection — scikit-learn 1.7.2 do...
Tools for model selection, such as cross validation and hyper-parameter tuning. User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, ...scikit-learn.org/stable/api/sklearn.model_selection.html -
sklearn.feature_selection — scikit-learn 1.7.2 ...
Feature selection algorithms. These include univariate filter selection methods and the recursive feature elimination algorithm. User guide. See the Feature selection section for further details.scikit-learn.org/stable/api/sklearn.feature_selection.html -
check_random_state — scikit-learn 1.7.2 documen...
Gallery examples: Empirical evaluation of the impact of k-means initialization MNIST classification using multinomial logistic + L1 Manifold Learning methods on a severed sphere Isotonic Regression...scikit-learn.org/stable/modules/generated/sklearn.utils.check_random_state.html -
sklearn.neural_network — scikit-learn 1.7.2 doc...
Models based on neural networks. User guide. See the Neural network models (supervised) and Neural network models (unsupervised) sections for further details.scikit-learn.org/stable/api/sklearn.neural_network.html