Python In Practice - 15 Projects to Master Python - How to Make Training and Testing Sets Easily

The video tutorial explains how to split a dataset into training and testing sets using the train_test_split function from sklearn. It covers importing the function, specifying input and output values, setting the ratio for splitting, and storing the results in variables. The tutorial emphasizes the importance of correct order and offers a comparison between manual slicing and using the function for efficiency.

What is the primary purpose of splitting data into training and testing sets?

• To ensure data is not lost

• To reduce the complexity of the model

• To evaluate the model's performance

• To increase the size of the dataset

Which library provides the train_test_split function?

• matplotlib

• sklearn

• numpy

• pandas

What is the correct syntax to import the train_test_split function?

• from sklearn import train_test_split

• from sklearn.model_selection import train_test_split

• import train_test_split from sklearn

• train_test_split from sklearn.model_selection

What does the test_size parameter in train_test_split specify?

• The number of features in the dataset

• The percentage of data to be used as the testing set

• The number of samples in the training set

• The size of the output variable

Why is it important to maintain the correct order of inputs and outputs when using train_test_split?

• To ensure the function runs faster

• To reduce the size of the dataset

• To avoid errors in data storage

• To increase the accuracy of the model