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

This example shows that imputing the missing values can give better results than discarding the samples containing any missing value. Imputing

The

This example shows how to use cross_val_predict to visualize prediction errors.

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

This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half

This example constructs a pipeline that does dimensionality reduction followed by prediction with a support vector classifier

An illustration of the isotonic regression on generated data. The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared

This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process: pick

Datasets can often contain components of that require different feature extraction and processing pipelines. This scenario might occur when: