Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

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.

The following steps will help you accomplish the task:

  1. Open a Colab notebook to the code for Exercise 6.12 and continue writing your code.
  2. Predict...