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Effect of transforming the targets in regressio...
In this example, we give an overview of TransformedTargetRegressor. We use two examples to illustrate the benefit of transforming the targets before learning a linear regression model. The first ex...scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html -
Compare Stochastic learning strategies for MLPC...
This example visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints, we use several small datasets, for which L-BFGS ...scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html -
Test with permutations the significance of a cl...
This example demonstrates the use of permutation_test_score to evaluate the significance of a cross-validated score using permutations. Dataset: We will use the Iris plants dataset, which consists ...scikit-learn.org/stable/auto_examples/model_selection/plot_permutation_tests_for_classification.html -
Concatenating multiple feature extraction metho...
In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use Feat...scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html -
Outlier detection on a real data set — scikit-l...
This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure. We selected two ...scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_wine.html -
Multilabel classification using a classifier ch...
This example shows how to use ClassifierChain to solve a multilabel classification problem. The most naive strategy to solve such a task is to independently train a binary classifier on each label ...scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html -
make_column_transformer — scikit-learn 1.5.2 do...
Gallery examples: Release Highlights for scikit-learn 0.23 Categorical Feature Support in Gradient Boosting Combine predictors using stacking Common pitfalls in the interpretation of coefficients o...scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html -
fetch_lfw_people — scikit-learn 1.5.2 documenta...
Gallery examples: Faces recognition example using eigenfaces and SVMsscikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html -
Features in Histogram Gradient Boosting Trees —...
Histogram-Based Gradient Boosting(HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to Li...scikit-learn.org/stable/auto_examples/ensemble/plot_hgbt_regression.html -
enable_iterative_imputer — scikit-learn 1.5.2 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