Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel trick, i.e., they learn a linear function in the space induced by the respective kernel which corresponds to a non-linear function in the original space. They differ in the loss functions (ridge versus epsilon-insensitive loss). In contrast to SVR, fitting a KRR can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus sl

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

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

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.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

Section contents In this section, we introduce the machine learning vocabulary that we use throughout scikit-learn and give a simple learning example. Machine learning: the problem setting In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. We can separate lea

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

Warning DEPRECATED class sklearn.mixture.GMM(*args, **kwargs) [source] Legacy Gaussian Mixture Model Deprecated since version 0.18: This class will be removed in 0.20. Use sklearn.mixture.GaussianMixture instead. Methods aic(X) Akaike information criterion for the current model fit and the proposed data. bic(X) Bayesian information criterion for the current model fit and the proposed data. fit(X[, y]) Estimate model parameters with the EM algorithm. fit_predict(X[, y]) Fit and then

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.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