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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K-nearest neighbors

This algorithm belongs to a particular family called instance-based algorithms (the methodology is called instance-based learning).

It differs from other approaches because it doesn't work with an actual mathematical model. On the contrary, the inference is performed by direct comparison of new samples with existing ones (which are defined as instances). KNN is an approach that can be easily employed to solve clustering, classification, and regression problems (even though, in this case, we are going to consider only the first technique). The main idea behind the clustering algorithm is very simple. Let's consider a data generating process pdata and finite a dataset drawn from this distribution:

Each point has a dimensionality equal to N. We can now introduce a distance function , which in most cases can be generalized with the Minkowski distance:

When p = 2, represents the classical Euclidean distance, that is normally the ...