Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used

Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615

Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities

Demonstration of several covariances types for Gaussian mixture models. See

An illustration of the isotonic regression on generated data. The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared

This example constructs a pipeline that does dimensionality reduction followed by prediction with a support vector classifier

This example is based on Section 5.4.3 of ?Gaussian Processes for Machine Learning? [RW2006]. It illustrates an example of complex kernel

For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten

Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent. The coefficients can be forced to be positive.

Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. See