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

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

Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to discriminative learning of linear classifiers under convex loss functions

Warning All classifiers in scikit-learn do multiclass classification

The sklearn

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.

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

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

The class

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