Book Image

Statistics for Machine Learning

By : Pratap Dangeti
Book Image

Statistics for Machine Learning

By: Pratap Dangeti

Overview of this book

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Principal component analysis - PCA


Principal component analysis (PCA) is the dimensionality reduction technique which has so many utilities. PCA reduces the dimensions of a dataset by projecting the data onto a lower-dimensional subspace. For example, a 2D dataset could be reduced by projecting the points onto a line. Each instance in the dataset would then be represented by a single value, rather than a pair of values. In a similar way, a 3D dataset could be reduced to two dimensions by projecting variables onto a plane. PCA has the following utilities:

  • Mitigate the course of dimensionality
  • Compress the data while minimizing the information lost at the same time
  • Principal components will be further utilized in the next stage of supervised learning, in random forest, boosting, and so on
  • Understanding the structure of data with hundreds of dimensions can be difficult, hence, by reducing the dimensions to 2D or 3D, observations can be visualized easily

PCA can easily be explained with the following...