Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes? theorem with the ?naive? assumption of independence between every pair of features. Given

The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the input variables. In mathematical

Warning All classifiers in scikit-learn do multiclass classification

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised

The sklearn

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

The

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

The class

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