Train-Test Split for Evaluating Machine Learning Algorithms

Last Updated on August 26, 2020

The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.

It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.

In this tutorial, you will discover how to evaluate machine learning models using the train-test split.

After completing this tutorial, you will know:

• The train-test split procedure is appropriate when you have a very large dataset, a costly model to train, or require a good estimate of model performance quickly.
• How to use the scikit-learn machine learning library to perform the train-test split procedure.
• How to evaluate machine learning algorithms for classification and regression using the train-test split.

Kick-start your project with my new book Machine Learning Mastery With
To finish reading, please visit source site