2.9.1. Restricted Boltzmann machines Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic

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

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.

The

sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample

Warning This implementation is not intended for large-scale applications

Decision Trees (DTs) are a non-parametric supervised learning method used for

2.5.1. Principal component analysis (PCA) 2.5.1.1. Exact PCA and probabilistic interpretation PCA

Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques

The sklearn