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sklearn.frozen — scikit-learn 1.8.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version sklearn.frozen # FrozenEstimator Estimator that wraps...scikit-learn.org/stable/api/sklearn.frozen.html -
sklearn.multiclass — scikit-learn 1.8.0 documen...
Multiclass learning algorithms. one-vs-the-rest / one-vs-all, one-vs-one, error correcting output codes. The estimators provided in this module are meta-estimators: they require a base estimator to...scikit-learn.org/stable/api/sklearn.multiclass.html -
sklearn.svm — scikit-learn 1.8.0 documentation
Support vector machine algorithms. User guide. See the Support Vector Machines section for further details.scikit-learn.org/stable/api/sklearn.svm.html -
sklearn.tree — scikit-learn 1.8.0 documentation
Decision tree based models for classification and regression. User guide. See the Decision Trees section for further details. Exporting: Plotting:scikit-learn.org/stable/api/sklearn.tree.html -
sklearn.neighbors — scikit-learn 1.8.0 document...
The k-nearest neighbors algorithms. User guide. See the Nearest Neighbors section for further details.scikit-learn.org/stable/api/sklearn.neighbors.html -
sklearn.kernel_approximation — scikit-learn 1.8...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html -
sklearn.mixture — scikit-learn 1.8.0 documentation
Mixture modeling algorithms. User guide. See the Gaussian mixture models section for further details.scikit-learn.org/stable/api/sklearn.mixture.html -
sklearn.dummy — scikit-learn 1.8.0 documentation
Dummy estimators that implement simple rules of thumb. User guide. See the Metrics and scoring: quantifying the quality of predictions section for further details.scikit-learn.org/stable/api/sklearn.dummy.html -
sklearn.naive_bayes — scikit-learn 1.8.0 docume...
Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...scikit-learn.org/stable/api/sklearn.naive_bayes.html -
sklearn.random_projection — scikit-learn 1.8.0 ...
Random projection transformers. Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional ...scikit-learn.org/stable/api/sklearn.random_projection.html