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

class sklearn.gaussian_process.kernels.Hyperparameter

Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD

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

An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised

sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)

sklearn.datasets.load_files(container_path, description=None, categories=None, load_content=True, shuffle=True, encoding=None, decode_error='strict'

The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the input variables. In mathematical

This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can improve prediction accuracy on a multi-class problem. The classification dataset