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polynomial_kernel — scikit-learn 1.5.2 document...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>..., degree = 2 ) array([[1. , 1. ], [1.77..., 2.77...]]) On this...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html -
Semi Supervised Classification — scikit-learn 1...
Examples concerning the sklearn.semi_supervised module. Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset Effect of varying threshold for self-training Label Propagati...scikit-learn.org/stable/auto_examples/semi_supervised/index.html -
sklearn.utils — scikit-learn 1.5.2 documentation
Various utilities to help with development. Developer guide. See the Utilities for Developers section for further details. Input and parameter validation: Functions to validate input and parameters...scikit-learn.org/stable/api/sklearn.utils.html -
sklearn.svm — scikit-learn 1.5.2 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 1.5.2 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 1.5.2 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.mixture — scikit-learn 1.5.2 documentation
Mixture modeling algorithms. User guide. See the Gaussian mixture models section for further details.scikit-learn.org/stable/api/sklearn.mixture.html -
get_config — scikit-learn 1.5.2 documentation
Skip to main content Back to top Ctrl + K GitHub get_config # sklearn. get_config ( ) [source] # Retrieve current val...scikit-learn.org/stable/modules/generated/sklearn.get_config.html -
sklearn.multiclass — scikit-learn 1.5.2 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.dummy — scikit-learn 1.5.2 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