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

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.

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

The class

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

Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The