In order to test if a classification score is significative a technique in repeating the classification procedure after randomizing

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality using cross-validation. ROC

The plots display firstly what a K-means algorithm would yield using three clusters. It is then shown what the effect of a bad initialization is on the classification process:

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.

This example shows the use of forests of trees to evaluate the importance of the pixels in an image classification task (faces). The hotter

The dataset used in this example is a preprocessed excerpt of the ?Labeled Faces in the Wild?, aka

Finds core samples of high density and expands clusters from them. print(__doc__) import numpy as np from

Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with multivariate response, a.k.a. PLS2 - PLSRegression, with univariate