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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

Assessing Model Performance for Regression Models

When you create a regression model, you create a model that predicts a continuous numerical variable, as you learned in Chapter 2, Regression. When you set aside your evaluation dataset, you have something that you can use to compare the quality of your model.

What you need to do to assess your model quality is compare the quality of your prediction to what is called the ground truth, which is the actual observed value that you are trying to predict. Take a look at Figure 6.4, in which the first column contains the ground truth (called actuals) and the second column contains the predicted values:

Figure 6.4: Actual versus predicted values

Line 0 in the output compares the actual value in our evaluation dataset to what our model predicted. The actual value from our evaluation dataset is 4.891. The value that the model predicted is 4.132270.

Line 1 compares the actual value of 4.194 to what the model predicted...

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