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

A recursive feature elimination example showing the relevance of pixels in a digit classification task.

This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters

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

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different

Here a sine function is fit with a polynomial of order 3, for values close to zero. Robust fitting is demoed in different situations: No

Face, a 1024 x 768 size image of a raccoon face, is used here to illustrate how k-means is used for vector quantization.

In this example, an image with connected circles is generated and spectral clustering is used to separate the circles. In these settings, the

Show below is a logistic-regression classifiers decision boundaries on the

This example illustrates visually in the feature space a comparison by results using two different component analysis techniques.