The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that

Many statistical problems require at some point the estimation of a population?s covariance matrix, which can be seen as an estimation of data set scatter plot shape.

Warning This implementation is not intended for large-scale applications

The sklearn.datasets package embeds some small toy datasets as introduced in the

Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier)

Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. 2.2.1. Introduction High-dimensional datasets can be very difficult to visualize. While data in two or three dimensions can be plotted to show the inherent structure of the data, equivalent high-dimensional plots are much less intuitive. To aid visualization of the structure of a dataset, the dimension

The cross decomposition module contains two main families of algorithms: the partial least squares (PLS) and the canonical correlation analysis (CCA). These families

The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability

The sklearn

Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The