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sklearn.multioutput — scikit-learn 1.7.0 docume...
Multioutput regression and classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator extends ...scikit-learn.org/stable/api/sklearn.multioutput.html -
sklearn.pipeline — scikit-learn 1.7.0 documenta...
Utilities to build a composite estimator as a chain of transforms and estimators. User guide. See the Pipelines and composite estimators section for further details.scikit-learn.org/stable/api/sklearn.pipeline.html -
mean_shift — scikit-learn 1.7.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version mean_shift # sklearn.cluster. mean_shift ( X , * , ba...scikit-learn.org/stable/modules/generated/sklearn.cluster.mean_shift.html -
check_cv — scikit-learn 1.7.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version check_cv # sklearn.model_selection. check_cv ( cv = 5...scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html -
Generalized Linear Models — scikit-learn 1.7.0 ...
Examples concerning the sklearn.linear_model module. Comparing Linear Bayesian Regressors Curve Fitting with Bayesian Ridge Regression Decision Boundaries of Multinomial and One-vs-Rest Logistic Re...scikit-learn.org/stable/auto_examples/linear_model/index.html -
Importance of Feature Scaling — scikit-learn 1....
Feature scaling through standardization, also called Z-score normalization, is an important preprocessing step for many machine learning algorithms. It involves rescaling each feature such that it ...scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html -
Recursive feature elimination — scikit-learn 1....
This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html -
SGD: Penalties — scikit-learn 1.7.0 documentation
Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net. All of the above are supported by SGDClassifier and SGDRegressor. Total running time of the script:(0 min...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html -
SVM Margins Example — scikit-learn 1.7.0 docume...
The plots below illustrate the effect the parameter C has on the separation line. A large value of C basically tells our model that we do not have that much faith in our data’s distribution, and wi...scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html -
sklearn.ensemble — scikit-learn 1.7.0 documenta...
Ensemble-based methods for classification, regression and anomaly detection. User guide. See the Ensembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.scikit-learn.org/stable/api/sklearn.ensemble.html