A 1D regression with decision tree. The

Computes a Bayesian Ridge Regression on a synthetic dataset. See

When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence

Toy example of 1D regression using linear, polynomial and RBF kernels. print(__doc__)

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

An example illustrating the approximation of the feature map of an RBF kernel. It shows how to use

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

This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (RBF) kernel SVM. Intuitively, the gamma

The following example illustrates the effect of scaling the regularization parameter when using

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