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NearestCentroid — scikit-learn 1.7.1 documentation
np . array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1...1 , 1 ], [ 2 , 1 ], [ 3 , 2 ]]) >>> y = np . array ([ 1 , 1 ,...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html -
1.12. Multiclass and multioutput algorithms — s...
= np . array ([ 'apple' , 'pear' , 'apple' , 'orange' ]) >>> print...array ([ 'apple' , 'pear' , 'apple' , 'orange' ]) >>> y_dense = LabelBinarizer...scikit-learn.org/stable/modules/multiclass.html -
11. Common pitfalls and recommended practices —...
StandardScaler()), ('linearregression', LinearRegression())]) >>> mean_squared_error...mean_squared_error ( y_test , model . predict ( X_test )) 0.90... Pipelines...scikit-learn.org/stable/common_pitfalls.html -
14. External Resources, Videos and Talks — scik...
Videos and Talks # 14.1. The scikit-learn MOOC # If you are new to...scikit-learn MOOC (Massive Open Online Course) . The MOOC, created...scikit-learn.org/stable/presentations.html -
1.15. Isotonic regression — scikit-learn 1.7.1 ...
\hat{y}_j\) whenever \(X_i \le X_j\) , where the weights \(w_i\) are...Ctrl + K GitHub Choose version 1.15. Isotonic regression # The...scikit-learn.org/stable/modules/isotonic.html -
KBinsDiscretizer — scikit-learn 1.7.1 documenta...
>>> X = [[ - 2 , 1 , - 4 , - 1 ], ... [ - 1 , 2 , - 3 , - 0.5 ],...“hazen”, “weibull”, “linear”, “median_unbiased”, “normal_unbiased”},...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html -
PolynomialCountSketch — scikit-learn 1.7.1 docu...
1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...SGDClassifier(max_iter=10) >>> clf . score ( X_features , y ) 1.0 For...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html -
fastica — scikit-learn 1.7.1 documentation
to ‘unit-variance’ in 1.3. fun {‘logcosh’, ‘exp’, ‘cube’} or...neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You...scikit-learn.org/stable/modules/generated/fastica-function.html -
normalize — scikit-learn 1.7.1 documentation
array([[-0.4, 0.2, 0.4], [-0.5, 0. , 0.5]]) >>> normalize ( X , norm =...array([[-0.67, 0.33, 0.67], [-0.71, 0. , 0.71]]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html -
davies_bouldin_score — scikit-learn 1.7.1 docum...
[[ 0 , 1 ], [ 1 , 1 ], [ 3 , 4 ]] >>> labels = [ 0 , 0 , 1 ]...davies_bouldin_score ( X , labels ) 0.12... On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html