class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') [source] Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer The optimization objective for MultiTaskElasticNet is: (1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * l1_ratio * ||W||_21 + 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2 Where: ||W||_21 = \sum_i \sqrt{\sum

class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples='auto', contamination=0.1, max_features=1.0, bootstrap=False, n_jobs=1, random_state=None, verbose=0) [source] Isolation Forest Algorithm Return the anomaly score of each sample using the IsolationForest algorithm The IsolationForest ?isolates? observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Since recursive partitioning c

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None) [source] Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a ?__?. A transformer may be replaced entir

class sklearn.dummy.DummyClassifier(strategy='stratified', random_state=None, constant=None) [source] DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. Read more in the User Guide. Parameters: strategy : str, default=?stratified? Strategy to use to generate predictions. ?stratified?: generates predictions by respecting the training set?s class

class sklearn.linear_model.MultiTaskElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=1, random_state=None, selection='cyclic') [source] Multi-task L1/L2 ElasticNet with built-in cross-validation. The optimization objective for MultiTaskElasticNet is: (1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * l1_ratio * ||W||_21 + 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2 Where:

class sklearn.mixture.BayesianGaussianMixture(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10) [source] Variational Bayesian estimation of a Gaussian mixture. This class allows

class sklearn.ensemble.VotingClassifier(estimators, voting='hard', weights=None, n_jobs=1) [source] Soft Voting/Majority Rule classifier for unfitted estimators. New in version 0.17. Read more in the User Guide. Parameters: estimators : list of (string, estimator) tuples Invoking the fit method on the VotingClassifier will fit clones of those original estimators that will be stored in the class attribute self.estimators_. voting : str, {?hard?, ?soft?} (default=?hard?) If ?hard?, uses

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for the document classification example. You can adjust the number of categories by giving their names to the dataset loader or setting them to None to get the 20 of them. Here is a sample output of a run on a quad-core machine: Loading 20 newsgroups dataset for categories: ['alt.atheism', 'talk.religion.misc'] 1427 documents 2 categories Performing grid search... pi

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None) [source] Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm. Read more in the User Guide. Parameters: nu : float, optional (default=0.5) An upper bound on

class sklearn.ensemble.GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_split=1e-07, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto') [source] Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the