Book Image

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh
Book Image

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)

Error Analysis

Building an average model, as explained so far, is surprisingly easy through the use of the scikit-learn library. The key aspects of building an exceptional model come from the analysis and decision-making on the part of the researcher.

As we have seen so far, some of the most important tasks are choosing and pre-processing the dataset, determining the purpose of the study, and selecting the appropriate evaluation metric. After handling all of this and taking into account that a model needs to be fine-tuned in order to reach the highest standards, most data scientists recommend training a simple model, regardless of the hyperparameters, to get the study started.

Error analysis is then introduced as a very useful methodology to turn an average model into an exceptional one. As the name suggests, it consists of analyzing the errors among the different subsets of the dataset in order to target the condition that is affecting the model at a greater scale.