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Decision boundary of semi-supervised classifier...
base_classifier = SVC ( kernel = "rbf" , gamma = 0.5 , probability = True...] y = iris . target # step size in the mesh h = 0.02 rng = np...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
RadiusNeighborsClassifier — scikit-learn 1.7.0 ...
leaf_size = 30 , p = 2 , metric = 'minkowski' , outlier_label = None...radius = 1.0 , * , weights = 'uniform' , algorithm = 'auto' ,...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html -
MultiTaskLasso — scikit-learn 1.7.0 documentation
l1_ratio = 0.5 , eps = 0.001 , n_alphas = 100 , alphas = None ,...precompute = 'auto' , Xy = None , copy_X = True , coef_init = None...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html -
Pipelining: chaining a PCA and a logistic regre...
logistic = LogisticRegression ( max_iter = 10000 , tol = 0.1 ) pipe...ax0 , ax1 ) = plt . subplots ( nrows = 2 , sharex = True , figsize...scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html -
Inductive Clustering — scikit-learn 1.7.0 docum...
c = color , alpha = alpha , edgecolor = "k" ) # Generate..., y = make_blobs ( n_samples = N_SAMPLES , cluster_std = [ 1.0...scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html -
learning_curve — scikit-learn 1.7.0 documentation
exploit_incremental_learning = False , n_jobs = None , pre_dispatch = 'all' , verbose...verbose = 0 , shuffle = False , random_state = None , error_score...scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html -
PCA — scikit-learn 1.7.0 documentation
n_components == min ( n_samples , n_features ) If n_components == 'mle'...n_components == 'mle' will interpret svd_solver == 'auto' as svd_solver...scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html -
MiniBatchKMeans — scikit-learn 1.7.0 documentation
n_clusters = 8 , * , init = 'k-means++' , max_iter = 100 , batch_size...batch_size = 1024 , verbose = 0 , compute_labels = True , random_state...scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html -
make_regression — scikit-learn 1.7.0 documentation
tail_strength = 0.5 , noise = 0.0 , shuffle = True , coef = False , random_state...n_samples = 100 , n_features = 100 , * , n_informative = 10 , n_targets...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html -
fbeta_score — scikit-learn 1.7.0 documentation
labels = None , pos_label = 1 , average = 'binary' , sample_weight...average = "macro" , zero_division = np . nan , beta = 0.5 ) 0.128...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html