Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Other regression techniques

A brief introduction to following regression techniques comes next, and why you may prefer to use them in comparison to least squares. In this section, we'll cover:

  • Ridge regression, with a practical example in scikit-learn
  • Lasso and logistic regression
  • Polynomial regression with examples
  • Isotonic regression

One of the most common problems with linear regression is the ill-conditioning that causes instabilities in the solution. Ridge regression has been introduced to overcome this problem.

Ridge Regression

A very common problem in regression models arises as a result of the structure of XTX. We have previously shown that the presence of multi-collinearities forces , and this implies that the inversion becomes extremely problematic. A simple way to check the presence of multi-collinearities is based on the computation of the condition number of XTX (with normalized columns with a length equal to 1), defined as:

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