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SVC — scikit-learn 1.8.0 documentation
[ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...>>> y = np . array ([ 1 , 1 , 2 , 2 ]) >>> from sklearn.svm...scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.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 -
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 -
mean_pinball_loss — scikit-learn 1.8.0 do...
2 , 3 ] >>> mean_pinball_loss...mean_pinball_loss ( y_true , [ 0 , 2 , 3 ], alpha = 0.1 ) 0.03... >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_pinball_loss.html -
NearestNeighbors — scikit-learn 1.8.0 doc...
2 , return_distance = False ) array([[2, 0]]...) >>>...array([[2]])) As you can see, it returns [[0.5]], and [[2]], which...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
manhattan_distances — scikit-learn 1.8.0 ...
2 ], [ 3 , 4 ]], [[ 1 , 2 ], [ 0 , 3 ]]) array([[0., 2.],...manhattan_distances ([[ 3 ]], [[ 2 ]]) array([[1.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html -
Incremental PCA — scikit-learn 1.8.0 docu...
target n_components = 2 ipca = IncrementalPCA ( n_components...target_name in zip ( colors , [ 0 , 1 , 2 ], iris . target_names ): plt...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
gen_batches — scikit-learn 1.8.0 document...
list ( gen_batches ( 2 , 3 )) [slice(0, 2, None)] >>>...gen_batches ( 7 , 3 , min_batch_size = 2 )) [slice(0, 3, None), slice(3,...scikit-learn.org/stable/modules/generated/sklearn.utils.gen_batches.html -
1.5. Stochastic Gradient Descent — scikit...
:= \frac{1}{2} \sum_{j=1}^{m} w_j^2 = ||w||_2^2\) , \(L_1\) norm:...>>> clf . predict ([[ 2. , 2. ]]) array([1]) SGD fits a...scikit-learn.org/stable/modules/sgd.html -
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