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

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

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

The

Warning This implementation is not intended for large-scale applications

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

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

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

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

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