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)

Evaluation Metrics

Model evaluation is indispensable for creating effective models that not only perform well on the data that was used to train the model but also on unseen data. The task of evaluating the model is especially easy when dealing with supervised learning problems, where there is a ground truth that can be compared against the prediction of the model.

Determining the accuracy percentage of the model is crucial for its application to unseen data that does not have a label class to compare to. For example, a model with an accuracy of 98% may allow the user to assume that the odds of having an accurate prediction are high, and hence the model should be trusted.

The evaluation of performance, as mentioned previously, should be done on the validation set (dev set) to fine-tune the model, and on the test set to determine the expected performance of the selected model on unseen data.

Evaluation Metrics for Classification Tasks

A classification task refers to a model...