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non_negative_factorization — scikit-learn 1.6.0...
||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.non_negative_factorization.html -
d2_absolute_error_score — scikit-learn 1.6.0 do...
2 , 3 ] >>> y_pred = [ 2 , 2 , 2 ] >>> d2_absolute_error_score...3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>> d...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html -
Joint feature selection with multi-task Lasso —...
2 , 2 ) plt . spy ( coef_multi_task_lasso_...)) times = np . linspace ( 0 , 2 * np . pi , n_tasks ) for k in...scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html -
AdditiveChi2Sampler — scikit-learn 1.6.0 docume...
sample_steps = 2 , sample_interval = None ) [source]...original space is transformed into 2*sample_steps-1 features, where...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html -
Understanding the decision tree structure — sci...
3] = 2.4) > 0.800000011920929) decision node 2 : (X_test[0,...0.800000011920929 else to node 2. node=1 is a leaf node with value=[[1....scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html -
randomized_range_finder — scikit-learn 1.6.0 do...
size = 2 , n_iter = 2 , random_state = 42 ) array([[-0.21...,...no normalization if n_iter <= 2 and switches to LU otherwise....scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_range_finder.html -
Comparison of LDA and PCA 2D projection of Iris...
the different samples on the 2 first principal components. Linear...target_names pca = PCA ( n_components = 2 ) X_r = pca . fit ( X ) . transform...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
resample — scikit-learn 1.6.0 documentation
2)> >>> X_sparse . toarray () array([[1., 0.], [2., 1.],...= np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>> y =...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
Sum — scikit-learn 1.6.0 documentation
takes two kernels \(k_1\) and \(k_2\) and combines them via \[k_{sum}(X,...\[k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)\] Note that the __add__ magic...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Sum.html -
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