Machine Learning with Python: The Basics by David V

Author:David V. [V., David]
Language: eng
Format: epub
Published: 2017-03-22T07:00:00+00:00

This results when one tries to describe objects via the collection of features. The problem with high dimensionality of data is that the algorithms might be expected to take in too many features. Having many dimensions may be an indication that the data points are located far from each other point, and the distance between data points can be uninformative.

The Principle Component Analysis (PCA) will help us solve this problem. It works by finding a linear combination of two variables which contains much of the information. This principle component or the new variable can be used for the replacement of your two original variables. You can see it as a linear transformation method for yielding directions for maximizing variance of data.

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