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Logistic function — scikit-learn 1.7.2 document...
1 ]) plt . ylim ( - 0.25 , 1.25 ) plt . xlim...classify values as either 0 or 1, i.e. class one or two, using...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html -
RadiusNeighborsRegressor — scikit-learn 1.7.2 d...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...[[ 0 ], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>> from...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html -
Incremental PCA — scikit-learn 1.7.2 documentation
scatterpoints = 1 ) plt . axis ([ - 4 , 4 , - 1.5 , 1.5 ]) plt . show...target_name in zip ( colors , [ 0 , 1 , 2 ], iris . target_names ):...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
GMM covariances — scikit-learn 1.7.2 documentation
shape [ 1 ]) * gmm . covariances_ [ n ]...]) angle = np . arctan2 ( u [ 1 ], u [ 0 ]) angle = 180 * angle...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
scale — scikit-learn 1.7.2 documentation
independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>> scale ( X...scale >>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>> scale ( X ,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html -
FeatureUnion — scikit-learn 1.7.2 documentation
svd__n_components = 1 ) . fit_transform ( X ) array([[-1.5 , 3.04], [ 1.5 , 5.72]])...unchanged. Added in version 1.1: Added the option "passthrough"...scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html -
Binarizer — scikit-learn 1.7.2 documentation
( X ) array([[1., 0., 1.], [1., 0., 0.], [0., 1., 0.]]) fit (...= [[ 1. , - 1. , 2. ], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , -...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html -
LassoLars — scikit-learn 1.7.2 documentation
([[ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ]], [ - 1 , 0 , - 1 ]) LassoLars(alpha=0.01)...sklearn.linear_model. LassoLars ( alpha = 1.0 , * , fit_intercept = True ,...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html -
Lars — scikit-learn 1.7.2 documentation
n_nonzero_coefs = 1 ) >>> reg . fit ([[ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ]], [...[ - 1.1111 , 0 , - 1.1111 ]) Lars(n_nonzero_coefs=1) >>> print...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html -
DictionaryLearning — scikit-learn 1.7.2 documen...
* || U || _1 , 1 ( U , V ) with || V_k || _2 <= 1 for all 0 <=...the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix...scikit-learn.org/stable/modules/generated/sklearn.decomposition.DictionaryLearning.html