This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two ?Gaussian quantiles? clusters (see

This example uses

This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3

sklearn.datasets.load_svmlight_file(f, n_features=None, dtype=, multilabel=False, zero_based='auto', query_id=False)

class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1

sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([ 0.1, 0.33, 0.55, 0.78, 1. ]), cv=None

Modeling species? geographic distributions is an important problem in conservation biology. In this example we model the geographic distribution of two south american

sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', filter_params=False, n_jobs=1, **kwds)

class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True,

sklearn.svm.libsvm.fit() Train the model using libsvm (low-level method)