This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn?t fit into

This examples shows the use of forests of trees to evaluate the importance of features on an artificial classification task. The red bars are the feature

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)

In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm.

RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different

An example using a one-class SVM for novelty detection.

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM. This demonstrates

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference in performance between the discrete SAMME [2] boosting algorithm