This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification

An example using IsolationForest for anomaly detection. The IsolationForest ?isolates? observations by randomly selecting a feature and then randomly selecting

Plot the contours of the three penalties. All of the above are supported by sklearn.linear_model.stochastic_gradient.

An example showing how different online solvers perform on the hand-written digits dataset.

Plot the confidence ellipsoids of each class and decision boundary print(__doc__)

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat.

When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some

An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online

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

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