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)
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Evaluation metrics

In many cases, it's impossible to evaluate the performance of a clustering algorithm using only a visual inspection. Moreover, it's important to use standard objective metrics that allow us to compare different approaches.

We are now going to introduce some methods based on the knowledge of the ground truth (the correct assignment for each data point) and one common strategy employed when the true labels are unknown.

Before discussing the scoring functions, we need to introduce a standard notation. If there are k clusters, we define the true labels as:

In the same way, we can define the predicted labels:

Both sets can be considered as sampled from two discrete random variables (for simplicity, we denote them with the same names), whose probability mass functions are and with a generic (yi represents the index of the ith cluster). These two probabilities can be approximated with a frequency count; so, for example, the probability...