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randomized_svd — scikit-learn 1.6.1 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version randomized_svd # sklearn.utils.extmath. randomized_sv...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_svd.html -
make_checkerboard — scikit-learn 1.6.1 document...
Gallery examples: A demo of the Spectral Biclustering algorithmscikit-learn.org/stable/modules/generated/sklearn.datasets.make_checkerboard.html -
show_versions — scikit-learn 1.6.1 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version show_versions # sklearn. show_versions ( ) [source] #...scikit-learn.org/stable/modules/generated/sklearn.show_versions.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 -
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 1.6.1 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 1.6.1 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 -
Nearest Centroid Classification — scikit-learn ...
Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.,., Total running time of the script:(0 minutes 0.168 seconds) Launch binder Launch JupyterLite ...scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html -
sklearn.ensemble — scikit-learn 1.6.1 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 -
sklearn.multioutput — scikit-learn 1.6.1 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