Discuss the importance of data : Test-Train split in Python

The video tutorial explains the concept of test train split, a method used to divide data into training and testing sets, typically in an 80-20 ratio. It highlights the importance of using the sklearn library for this process and introduces the random set parameter to ensure consistent data splits for model evaluation. The tutorial also covers the outputs of the train test split function, including X train, X test, Y train, and Y test, and their roles in model training and evaluation.

Describe the outputs you get from the train test split function.

Evaluate responses using AI:

OFF

Why is it important to check the shape of your training and testing data?

Evaluate responses using AI:

OFF

What should be the relationship between the number of observations in X train and X test?

Evaluate responses using AI:

OFF