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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

Tuning Using Grid Search

In the context of machine learning, grid search refers to a strategy of systematically testing out every hyperparameterization from a pre-defined set of possibilities for your chosen estimator. You decide the criteria used to evaluate performance, and once the search is complete, you may manually examine the results and choose the best hyperparameterization, or let your computer automatically choose it for you.

The overall objective is to try and find an optimal hyperparameterization that leads to improved performance when predicting unseen data.

Before we get to the implementations of grid search in scikit-learn, let's first demonstrate the strategy using simple Python for loops.

Simple Demonstration of the Grid Search Strategy

In the following demonstration of the grid search strategy, we will use the breast cancer prediction dataset we saw in Exercise 8.01, Manual Hyperparameter Tuning for a k-NN Classifier, where we manually tuned the...

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