Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers

Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the

sklearn.metrics.completeness_score(labels_true, labels_pred)

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

An example showing univariate feature selection. Noisy (non informative) features are added to the iris data and univariate feature selection is applied

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

Gaussian Processes (GP) are a generic supervised learning method designed to solve regression and probabilistic classification problems