Book Image

The Data Science Workshop - Second Edition

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
5 (1)
Book Image

The Data Science Workshop - Second Edition

5 (1)
By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
Preface
12
12. Feature Engineering

Maximum Features

We are getting close to the end of this chapter. You have already learned how to tune several of the most important hyperparameters for RandomForest. In this section, we will present you with another extremely important one: max_features.

Earlier, we learned that RandomForest builds multiple trees and takes the average to make predictions. This is why it is called a forest, but we haven't really discussed the "random" part yet. Going through this chapter, you may have asked yourself: how does building multiple trees help to get better predictions, and won't all the trees look the same given that the input data is the same?

Before answering these questions, let's use the analogy of a court trial. In some countries, the final decision of a trial is either made by a judge or a jury. A judge is a person who knows the law in detail and can decide whether a person has broken the law or not. On the other hand, a jury is composed of people from...