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Gradient Boosting regularization — scikit-learn...
2" , "turquoise" , { "learning_rate" : 0.2 , "subsample"..."learning_rate=0.2, subsample=0.5" , "gray" , { "learning_rate" : 0.2 , "subsample"...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html -
hinge_loss — scikit-learn 1.7.2 documentation
([[ - 2 ], [ 3 ], [ 0.5 ]]) >>> pred_decision array([-2.18, 2.36,...[ 1 ], [ 2 ], [ 3 ]]) >>> Y = np . array ([ 0 , 1 , 2 , 3 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html -
Nested versus non-nested cross-validation — sci...
This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters al...scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html -
enet_path — scikit-learn 1.7.2 documentation
it is: 1 / ( 2 * n_samples ) * || y - Xw ||^ 2_2 + alpha * l1_ratio...|| w ||^ 2_2 For multi-output tasks it is: ( 1 / ( 2 * n_samples...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html -
unique_labels — scikit-learn 1.7.2 documentation
2 , 3 , 4 ], [ 2 , 2 , 3 , 4 ]) array([1, 2, 3, 4]) >>>...unique_labels ([ 1 , 2 , 10 ], [ 5 , 11 ]) array([ 1, 2, 5, 10, 11])...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.unique_labels.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.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 -
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 -
l1_min_c — scikit-learn 1.7.2 documentation
Gallery examples: Regularization path of L1- Logistic Regressionscikit-learn.org/stable/modules/generated/sklearn.svm.l1_min_c.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