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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

Summary

In this chapter, we have covered three strategies for hyperparameter tuning based on searching for estimator hyperparameterizations that improve performance.

The manual search is the most hands-on of the three but gives you a unique feel for the process. It is suitable for situations where the estimator in question is simple (a low number of hyperparameters).

The grid search is an automated method that is the most systematic of the three but can be very computationally intensive to run when the range of possible hyperparameterizations increases.

The random search, while the most complicated to set up, is based on sampling from distributions of hyperparameters, which allows you to expand the search range, thereby giving you the chance to discover a good solution that you may miss with the grid or manual search options.

In the next chapter, we will be looking at how to visualize results, summarize models, and articulate feature importance and weights.

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