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

Semi-supervised learning is a situation in which

Support vector machines (SVMs) are a set of supervised learning methods used for

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

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 sklearn

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

The class

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