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SGD: Weighted samples — scikit-learn 1.7.0 docu...
c = y , s = sample_weight , alpha = 0.9 , cmap = plt . cm...alpha = 0.01 , max_iter = 100 ) clf . fit ( X , y ) Z = clf ....scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html -
beta_divergence.png
encoding=ISO-8859-1, compression=none keyword=Software, value=Matplotlib...0.2540005 width=640, height=480, bitDepth=8, colorType=RGB, compr...scikit-learn.org/stable/_images/beta_divergence.png -
RationalQuadratic — scikit-learn 1.7.0 document...
length_scale = 1.0 , alpha = 1.0 , length_scale_bounds = (1e-05, 100000.0)...>>> X , y = load_iris ( return_X_y = True ) >>> kernel = RationalQuadratic...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html -
1.7. Gaussian Processes — scikit-learn 1.7.0 do...
for the WhiteKernel ): k(X) == K(X, Y=X) If only the diagonal of...bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) Hyper...scikit-learn.org/stable/modules/gaussian_process.html -
Sparse inverse covariance estimation — scikit-l...
cov /= d cov /= d [:, np . newaxis ] prec *= d prec *= d [:,..., size = n_samples ) X -= X . mean ( axis = 0 ) X /= X . std...scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html -
SVM Tie Breaking Example — scikit-learn 1.7.0 d...
()): svm = SVC ( kernel = "linear" , C = 1 , break_ties = break_ties...SVC X , y = make_blobs ( random_state = 27 ) fig , sub = plt . subplots...scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html -
mutual_info_classif — scikit-learn 1.7.0 docume...
discrete_features = 'auto' , n_neighbors = 3 , copy = True , random_state...n_features = 10 , n_informative = 2 , n_clusters_per_class = 1 , ......scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html -
explained_variance_score — scikit-learn 1.7.0 d...
sample_weight = None , multioutput = 'uniform_average' ,... >>> y_true = [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0...scikit-learn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html -
EmpiricalCovariance — scikit-learn 1.7.0 docume...
multivariate_normal ( mean = [ 0 , 0 ], ... cov = real_cov , ... size = 500 ) >>>...comp_cov , norm = 'frobenius' , scaling = True , squared = True ) [source]...scikit-learn.org/stable/modules/generated/sklearn.covariance.EmpiricalCovariance.html -
Nearest Centroid Classification — scikit-learn ...
c = y , cmap = cmap_bold , edgecolor = "k" , s = 20 ) plt...shrinkage , np . mean ( y == y_pred )) _ , ax = plt . subplots () DecisionBoundaryDisp...scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html
