The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations

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

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

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors.

Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. We use a GridSearchCV to

In this example we see how to robustly fit a linear model to faulty data using the RANSAC 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

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

Demonstrate how model complexity influences both prediction accuracy and computational performance. The dataset is the Boston Housing dataset (resp. 20 Newsgroups)