Understanding Train-test-split Function

The video tutorial explains the process of preparing data for machine learning, focusing on splitting data into input and output variables, training and testing models, and using the train test split function to automate data splitting. It covers the importance of data preparation, model accuracy, and practical data handling techniques.

Why is it important to remove irrelevant columns, such as the name column, when preparing data for machine learning?

• To increase the number of features

• To reduce the size of the dataset

• To improve the model's ability to find patterns

• To make the dataset more visually appealing

What are the input and output variables referred to in coding terms?

• A and B

• X and Y

• Input and Output

• M and N

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

• To increase the size of the dataset

• To test the model's accuracy

• To make the dataset more complex

• To simplify the data preparation process

What is a common ratio for splitting data into training and testing sets?

• 90:10

• 50:50

• 60:40

• 70:30

What does the train_test_split function return?

• Two arrays

• Three arrays

• Four arrays

• Five arrays

Which parameter is NOT required when using the train_test_split function?

• Output Y

• Model type

• Input X

• Percentage for splitting

What is the main advantage of using the train_test_split function?

• It improves model accuracy

• It reduces the number of features

• It increases the dataset size

• It automates the data splitting process