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)


The cross_val_score() function is available in sklearn.model_selection. Up until this point, you have learned how to create cross-validation datasets in a loop. If you made use of that approach, you would need to keep track of all of the models that you are training and evaluating inside of that loop.

cross_val_score takes care of the following:

  • Creating cross-validation datasets
  • Training models by fitting them to the training data
  • Evaluating the models on the validation data
  • Returning a list of the R2 score of each model that is trained

For all of the preceding actions to happen, you will need to provide the following inputs:

  • An instance of an estimator (for example, LinearRegression)
  • The original dataset
  • The number of splits to create (which is also the number of models that will be trained and evaluated)

Exercise 7.05: Getting the Scores from Five-Fold Cross-Validation

The goal of this exercise is to create...