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)

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...