Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient vector, and this is displayed as a function of the regularization parameter. The right plot shows how exact the solution is. This example illustrates how a well defined solution is found by Ridge regression and how regularization affects the coefficients and their values. The plot on the right shows how the difference of the coefficients from the estimator c

Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases

Demonstrates an active learning technique to learn handwritten digits using label propagation. We start by training a label propagation model

A tutorial exercise using Cross-validation with an SVM on the Digits dataset. This exercise is used in the

Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a

This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions

A plot that compares the various convex loss functions supported by

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

Plot decision function of a weighted dataset, where the size of points is proportional to its weight. The sample weighting rescales the C parameter, which means