Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By : Tarek Amr
Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By: Tarek Amr

Overview of this book

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
Table of Contents (18 chapters)
1
Section 1: Supervised Learning
8
Section 2: Advanced Supervised Learning
13
Section 3: Unsupervised Learning and More

Detecting outliers using isolation forest

In previous approaches, we started by defining what normal is, and then considered anything that doesn't conform to this as outliers. The isolation forest algorithm follows a different approach. Since the outliers are few and different, they are easier to isolate from the rest. So, when building a forest of random trees, a sample that ends in leaf nodes early in a tree—that is, it did not need a lot of branching effort to be isolated—is more likely to be an outlier.

As a tree-based ensemble, this algorithm shares many hyperparameters with its counterparts, such as the number of random trees to build (n_estimators), the ratio of samples to use when building each tree (max_samples), the ratio of features to consider when building each tree (max_features), and whether to sample with a replacement or not (bootstrap). You can also build the trees in parallel using all the available CPUs on your machine by setting...