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Confusion matrix — scikit-learn 1.7.2 doc...
Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is e...scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html -
SGD: Penalties — scikit-learn 1.7.2 docum...
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 -
Multioutput methods — scikit-learn 1.7.2 ...
Examples concerning the sklearn.multioutput module. Multilabel classification using a classifier chainscikit-learn.org/stable/auto_examples/multioutput/index.html -
sklearn.ensemble — scikit-learn 1.7.2 doc...
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 -
sklearn.multioutput — scikit-learn 1.7.2 ...
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 1.7.2 doc...
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 -
sklearn.covariance — scikit-learn 1.7.2 d...
Methods and algorithms to robustly estimate covariance. They estimate the covariance of features at given sets of points, as well as the precision matrix defined as the inverse of the covariance. C...scikit-learn.org/stable/api/sklearn.covariance.html -
sklearn.inspection — scikit-learn 1.7.2 d...
Tools for model inspection. User guide. See the Inspection section for further details. Plotting:scikit-learn.org/stable/api/sklearn.inspection.html -
sklearn.manifold — scikit-learn 1.7.2 doc...
Data embedding techniques. User guide. See the Manifold learning section for further details.scikit-learn.org/stable/api/sklearn.manifold.html -
sklearn.kernel_ridge — scikit-learn 1.7.2...
Kernel ridge regression. User guide. See the Kernel ridge regression section for further details.scikit-learn.org/stable/api/sklearn.kernel_ridge.html