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Pipelines and composite estimators — scikit-lea...
Examples of how to compose transformers and pipelines from other estimators. See the User Guide. Column Transformer with Heterogeneous Data Sources Column Transformer with Mixed Types Concatenating...scikit-learn.org/stable/auto_examples/compose/index.html -
SGD: Penalties — scikit-learn 1.5.2 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 -
classification_report — scikit-learn 1.5.2 docu...
Gallery examples: Recognizing hand-written digits Faces recognition example using eigenfaces and SVMs Pipeline ANOVA SVM Custom refit strategy of a grid search with cross-validation Restricted Bolt...scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html -
r2_score — scikit-learn 1.5.2 documentation
Gallery examples: L1-based models for Sparse Signals Linear Regression Example Non-negative least squares Failure of Machine Learning to infer causal effects Effect of transforming the targets in r...scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html -
log_loss — scikit-learn 1.5.2 documentation
Gallery examples: Probability Calibration curves Probability Calibration for 3-class classification Gradient Boosting Out-of-Bag estimates Gradient Boosting regularization Probabilistic predictions...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
roc_curve — scikit-learn 1.5.2 documentation
Gallery examples: Species distribution modeling Visualizations with Display Objects Detection error tradeoff (DET) curve Multiclass Receiver Operating Characteristic (ROC)scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html -
det_curve — scikit-learn 1.5.2 documentation
Gallery examples: Detection error tradeoff (DET) curvescikit-learn.org/stable/modules/generated/sklearn.metrics.det_curve.html -
sklearn.covariance — scikit-learn 1.5.2 documen...
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.multioutput — scikit-learn 1.5.2 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.ensemble — scikit-learn 1.5.2 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