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.

What is the typical percentage of data used for testing in a test train split?

• 20%

• 10%

• 40%

• 30%

Which library is commonly used to perform a test train split in Python?

• NumPy

• Pandas

• Sklearn

• TensorFlow

What parameter ensures that the test train split is consistent across different runs?

• Test size

• Random set

• X value

• Y value

If you want to achieve the same test train split as someone else, what should you do?

• Use the same test size

• Use the same X and Y values

• Use the same data set

• Use the same random set value

What are the four outputs of the train test split function?

• X_train, X_test, Y_train, Y_test

• X_val, X_test, Y_train, Y_test

• X_train, X_val, Y_train, Y_val

• X_train, X_test, Y_val, Y_test

How can you verify the correctness of your test train split?

• By checking the shape and content of the datasets

• By checking the random set value

• By checking the data types

• By checking the test size

What percentage of the total data is typically used for training in a test train split?

• 60%

• 50%

• 80%

• 70%