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plot_release_highlights_1_5_0.py
array( [ [-1.1, 1.1, 1.1], [3.9, -1.2, np.nan], [np.nan, 1.3, np.nan],...np.nan], [-0.1, -1.4, -1.4], [-4.9, 1.5, -1.5], [np.nan, 1.6, 1.6], ]...scikit-learn.org/stable/_downloads/ba0cfc16d7953e1c2c6912b6beca1e91/plot_release_highlights_1_5_0.py -
plot_tree — scikit-learn 1.6.0 documentation
“x[1]”, …). class_names array-like...scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html -
1.2. Linear and Quadratic Discriminant Analysis...
equal to \(\frac{1}{n - 1} X_k^tX_k = \frac{1}{n - 1} V S^2 V^t\)...\frac{1}{(2\pi)^{d/2} |\Sigma_k|^{1/2}}\exp\left(-\frac{1}{2} (x-\mu_k)^t...scikit-learn.org/stable/modules/lda_qda.html -
ConstantKernel — scikit-learn 1.6.0 documentation
\[k(x_1, x_2) = constant\_value \;\forall\; x_1, x_2\] Adding...predict ( X [: 1 ,:], return_std = True ) (array([606.1...]), array([0.24...]))...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html -
kmeans_plusplus — scikit-learn 1.6.0 documentation
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 10 , 2...provided array. Added in version 1.3. x_squared_norms array-like...scikit-learn.org/stable/modules/generated/sklearn.cluster.kmeans_plusplus.html -
fetch_20newsgroups — scikit-learn 1.6.0 documen...
1, 1, 1, 0, 1, 1, 0, 0, 0]) Gallery examples...Added in version 1.5. delay float, default=1.0 Number of seconds...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html -
Release Highlights for scikit-learn 1.6 — sciki...
1 , 6 , np . nan ]) . reshape ( - 1 , 1 ) y = [ 0 ,..., 0 , 1 , 1 ] forest = ExtraTreesClassifier ( random_state =...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html -
1.14. Semi-supervised learning — scikit-learn 1...
1.14.1. Self Training # This self-training...Ctrl + K GitHub Choose version 1.14. Semi-supervised learning #...scikit-learn.org/stable/modules/semi_supervised.html -
adjusted_rand_score — scikit-learn 1.6.0 docume...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjusted_rand_score...adjusted_rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
mutual_info_score — scikit-learn 1.6.0 document...
1 , 1 , 0 , 1 , 0 ] >>> labels_pred = [ 0 , 1 , 0 , 0...as: \[MI(U,V)=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i\cap...scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html