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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas 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)
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Preface
12
12. Feature Engineering

Introduction

In previous chapters, we discussed several methods to arrive at a model that performs well. These include transforming the data via preprocessing, feature engineering and scaling, or simply choosing an appropriate estimator (algorithm) type from the large set of possible estimators made available to the users of scikit-learn.

Depending on which estimator you eventually select, there may be settings that can be adjusted to improve overall predictive performance. These settings are known as hyperparameters, and deriving the best hyperparameters is known as tuning or optimizing. Properly tuning your hyperparameters can result in performance improvements well into the double-digit percentages, so it is well worth doing in any modeling exercise.

This chapter will discuss the concept of hyperparameter tuning and will present some simple strategies that you can use to help find the best hyperparameters for your estimators.

In previous chapters, we have seen some exercises...

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