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NMF — scikit-learn 1.8.0 documentation
||H||_{Fro}^2,\end{aligned}\end{align} \] where \(||A||_{Fro}^2 = \sum_{i,j}...array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html -
7.3. Preprocessing data — scikit-learn 1....
2. , 1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1],...4.1, 6.7, 2.5], [7.7, 4.2, 6.7, 2.5], [7.9, 4.4, 6.9, 2.5]]) Thus...scikit-learn.org/stable/modules/preprocessing.html -
LassoLarsIC — scikit-learn 1.8.0 document...
[[ - 2 , 2 ], [ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]] >>>...>>> y = [ - 2.2222 , - 1.1111 , 0 , - 1.1111 , - 2.2222 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html -
Ledoit-Wolf vs OAS estimation — scikit-le...
subplot ( 2 , 1 , 2 ) plt . errorbar ( n_samples_range...shrinkage_ # plot MSE plt . subplot ( 2 , 1 , 1 ) plt . errorbar ( n_samples_range...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html -
d2_absolute_error_score — scikit-learn 1....
2 , 3 ] >>> y_pred = [ 2 , 2 , 2 ] >>>...- 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html -
Theil-Sen Regression — scikit-learn 1.8.0...
Linear model y = 3*x + N(2, 0.1**2) x = np . random . randn (...Linear model y = 3*x + N(2, 0.1**2) x = np . random . randn (...scikit-learn.org/stable/auto_examples/linear_model/plot_theilsen.html -
DistanceMetric — scikit-learn 1.8.0 docum...
HaversineDistance 2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))...'euclidean' ) >>> X = [[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html -
FastICA on 2D point clouds — scikit-learn...
subplot ( 2 , 2 , 2 ) plot_samples ( X / np ....1.5 , size = ( 20000 , 2 )) S [:, 0 ] *= 2.0 # Mix data A = np ....scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html -
check_increasing — scikit-learn 1.8.0 doc...
2 , 3 , 4 , 5 ], [ 2 , 4 , 6 , 8 , 10 ] >>>...>>> y = [ 10 , 8 , 6 , 4 , 2 ] >>> check_increasing...scikit-learn.org/stable/modules/generated/sklearn.isotonic.check_increasing.html -
Classifier comparison — scikit-learn 1.8....
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state.... random . RandomState ( 2 ) X += 2 * rng . uniform ( size =...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html