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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 observed that some of the evaluation metrics for classification models require a binary classification model. We saw that when we worked with more than two classes, we were required to use the one-versus-all approach. The one-versus-all approach builds one model for each class and tries to predict the probability that the input belongs to a specific class. We saw that once this was done, we then predicted that the input belongs to the class where the model has the highest prediction probability. We also split our evaluation dataset into two, it's because X_test and y_test are used once for a final evaluation of the model's performance. You can make use of them before putting your model into production to see how the model would perform in a production environment.

You have learned how to assess the quality of a regression model by observing how the loss changes. You saw examples using the MAE, and also learned of the existence of MSE...

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