Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices.

Transform your features into a higher dimensional, sparse space. Then train a linear model on these features. First fit an ensemble of

In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain

A tutorial exercise for using different SVM kernels. This exercise is used in the

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width,

Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the

This data sets consists of 3 different types of irises? (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray The rows being the

This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class classification problem. Illustrated is the

The decision tree structure can be analysed to gain further insight on the relation between the features and the target to predict. In this example

Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel trick, i.e., they learn a linear function in the