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sklearn.svm.LinearSVC — scikit-learn 1.4.2 docu...
the parameter C of class i to class_weight[i]*C for SVC. If not...dual = 'warn' , tol = 0.0001 , C = 1.0 , multi_class = 'ovr' ,...scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html -
2.4. Biclustering — scikit-learn 1.4.2 document...
as follows: \[A_n = R^{-1/2} A C^{-1/2}\] Where \(R\) is the diagonal...to \(\sum_{j} A_{ij}\) and \(C\) is the diagonal matrix with...scikit-learn.org/stable/modules/biclustering.html -
sklearn.model_selection.permutation_test_score ...
calculated as: (C + 1) / (n_permutations + 1) Where C is the number...scikit-learn.org/stable/modules/generated/sklearn.model_selection.permutation_test_score.html -
sklearn.naive_bayes.ComplementNB — scikit-learn...
Returns : C ndarray of shape (n_samples,)...The input samples. Returns : C ndarray of shape (n_samples, n_classes)...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.ComplementNB.html -
sklearn.discriminant_analysis.LinearDiscriminan...
corresponds to sum_k prior_k * C_k where C_k is the covariance matrix...the samples in class k . The C_k are estimated using the (potentially...scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysi... -
6.7. Kernel Approximation — scikit-learn 1.4.2 ...
\prod_i \frac{2\sqrt{x_i+c}\sqrt{y_i+c}}{x_i + y_i + 2c}\] It has...free parameter, that is called \(c\) . For a motivation for this...scikit-learn.org/stable/modules/kernel_approximation.html -
4.1. Partial Dependence and Individual Conditio...
\mathbb{E}_{X_C}\left[ f(x_S, X_C) \right]\\ &= \int f(x_S, x_C) p(x_C)...p(x_C) dx_C,\end{split}\] where \(f(x_S, x_C)\) is the response function...scikit-learn.org/stable/modules/partial_dependence.html -
sklearn.neighbors.NearestCentroid — scikit-lear...
The predicted class C for each sample in X is returned....n_features) Test samples. Returns : C ndarray of shape (n_samples,)...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html -
feature_selection.rst.txt
the parameter C controls the sparsity: the smaller C the fewer features...(150, 4) >>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
sklearn.preprocessing.PolynomialFeatures — scik...
order {‘C’, ‘F’}, default=’C’ Order of output array...include_bias = True , order = 'C' ) [source] Generate polynomial...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html