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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

Cross-Validation

Consider an example where you split your data into five parts of 20% each. You would then make use of four parts for training and one part for evaluation. Because you have five parts, you can make use of the data five times, each time using one part for validation and the remaining data for training.

Figure 7.13: Cross-validation

Cross-validation is an approach to splitting your data where you make multiple splits and then make use of some of them for training and the rest for validation. You then make use of all of the combinations of data to train multiple models.

This approach is called n-fold cross-validation or k-fold cross-validation.

Note

For more information on k-fold cross-validation, refer to https://packt.live/36eXyfi.

KFold

The KFold class in sklearn.model_selection returns a generator that provides a tuple with two indices, one for training and another for testing or validation. A generator function lets you declare...

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