This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM. This demonstrates

Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features.

Out-of-bag (OOB) estimates can be a useful heuristic to estimate the ?optimal? number of boosting iterations. OOB estimates are almost identical to cross-validation

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

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference in performance between the discrete SAMME [2] boosting algorithm

This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-written digit. In order to utilize an 8x8 figure like this, we?d have to first

This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova feature

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

Compares FeatureHasher and DictVectorizer by using both to vectorize text documents. The example demonstrates syntax and speed only; it doesn