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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

Area Under the ROC Curve

The Area Under the Receiver Operating Characteristic Curve (ROC AUC) is a measure of the likelihood that the model will rank a randomly chosen positive example higher than a randomly chosen negative example. Another way of putting it is to say that the higher this measure is, the better the model is at predicting a negative class as negative, and a positive class as positive. The value ranges from 0 to 1. If the AUC is 0.6, it means that the model has a 60% probability of correctly distinguishing a negative class from a positive class based on the inputs. This measure is used to compare models.

Exercise 6.13: Computing the ROC AUC for the Caesarian Dataset

The goal of this exercise is to compute the ROC AUC for the binary classification model that you trained in Exercise 6.12, Computing and Plotting ROC Curve for a Binary Classification Problem.

Note

You should continue this exercise in the same notebook as that used in Exercise 6.12,...

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