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sklearn.base — scikit-learn 1.6.1 documentation
Base classes for all estimators and various utility functions.scikit-learn.org/stable/api/sklearn.base.html -
sklearn.isotonic — scikit-learn 1.6.1 documenta...
Isotonic regression for obtaining monotonic fit to data. User guide. See the Isotonic regression section for further details.scikit-learn.org/stable/api/sklearn.isotonic.html -
distance_metrics — scikit-learn 1.6.1 documenta...
Skip to main content Back to top Ctrl + K GitHub Choose version distance_metrics # sklearn.metrics.pairwise. distance...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.distance_metrics.html -
sklearn.compose — scikit-learn 1.6.1 documentation
Meta-estimators for building composite models with transformers. In addition to its current contents, this module will eventually be home to refurbished versions of Pipeline and FeatureUnion. User ...scikit-learn.org/stable/api/sklearn.compose.html -
sklearn.impute — scikit-learn 1.6.1 documentation
Transformers for missing value imputation. User guide. See the Imputation of missing values section for further details.scikit-learn.org/stable/api/sklearn.impute.html -
Working with text documents — scikit-learn 1.6....
Examples concerning the sklearn.feature_extraction.text module. Classification of text documents using sparse features Clustering text documents using k-means FeatureHasher and DictVectorizer Compa...scikit-learn.org/stable/auto_examples/text/index.html -
ParameterGrid — scikit-learn 1.6.1 documentation
[{ 'a' : 1 , 'b' : True }, { 'a' : 1 , 'b' : False },...( grid )[ 1 ] == { 'kernel' : 'rbf' , 'gamma' : 1 } True On this...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterGrid.html -
CategoricalNB — scikit-learn 1.6.1 documentation
Added in version 1.2. Changed in version 1.4: The default value... CategoricalNB ( * , alpha = 1.0 , force_alpha = True , fit_prior...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html -
OneToOneFeatureMixin — scikit-learn 1.6.1 docum...
This mixin assumes there’s a 1-to-1 correspondence between input...shape [ 1 ] ... return self >>> X = np . array ([[ 1 , 2 ], [...scikit-learn.org/stable/modules/generated/sklearn.base.OneToOneFeatureMixin.html -
ParameterSampler — scikit-learn 1.6.1 documenta...
'a' : 1 }, ... { 'b' : 0.923223 , 'a' : 1 }, ... { 'b' : 1.878964...0 ) >>> param_grid = { 'a' :[ 1 , 2 ], 'b' : expon ()} >>> param_list...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterSampler.html