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check_symmetric — scikit-learn 1.8.0 documentation
2 ], [ 1 , 0 , 1 ], [ 2 , 1 , 0 ]]) >>> check_symmetric...symmetric_array ) array([[0, 1, 2], [1, 0, 1], [2, 1, 0]]) >>> from scipy.sparse...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_symmetric.html -
train_test_split — scikit-learn 1.8.0 documenta...
2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5...1.4 0.2 122 7.7 2.8 6.7 2.0 >>> y_train . head () 96 1 105 2 66...scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html -
d2_log_loss_score — scikit-learn 1.8.0 document...
Like R^2, D^2 score may be negative (it need...labels = None ) [source] # \(D^2\) score function, fraction of...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_log_loss_score.html -
indexable — scikit-learn 1.8.0 documentation
2 , 3 ], np . array ([ 2 , 3 , 4 ]), None ,...indexable ( * iterables ) [[1, 2, 3], array([2, 3, 4]), None, <...Sparse...dtype...scikit-learn.org/stable/modules/generated/sklearn.utils.indexable.html -
1.5. Stochastic Gradient Descent — scikit-learn...
:= \frac{1}{2} \sum_{j=1}^{m} w_j^2 = ||w||_2^2\) , \(L_1\) norm:...values: >>> clf . predict ([[ 2. , 2. ]]) array([1]) SGD fits a...scikit-learn.org/stable/modules/sgd.html -
sphinx-design.min.css
sd-g-2,.sd-gy-2{--sd-gutter-y: 0.5rem}.sd-g-2,.sd-gx-2{--sd-gutter-x:...!important}.sd-p-2{padding:.5rem !important}.sd-pt-2,.sd-py-2{padding-top:.5rem...scikit-learn.org/stable/_static/sphinx-design.min.css -
resample — scikit-learn 1.8.0 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 -
incr_mean_variance_axis — scikit-learn 1....
2 , 2 ]) >>> data = np . array ([ 8 , 1 , 2 , 5...>>> scale = np . array ([ 2 , 3 , 2 ]) >>> csr = sparse...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.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 -
LinearRegression — scikit-learn 1.8.0 documenta...
2 ], [ 2 , 2 ], [ 2 , 3 ]]) >>> # y = 1 * x_0 + 2 * x_1...float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html