This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the Baye

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

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

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

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g

The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations

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

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

Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets

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.