PySpark and AWS: Master Big Data with PySpark and AWS - Train and Test Data

The video tutorial explains the process of splitting data into training and test sets, a common practice in AI algorithms like recommender systems. It demonstrates how to use the random split function in Python to divide data into 80% for training and 20% for testing. The tutorial also covers Python notation for data splitting and shows how to count rows in the resulting data frames.

Why is it important to split data into training and test sets when working with AI algorithms?

• To increase the complexity of the model

• To reduce the size of the dataset

• To test the model's performance after training

• To ensure data privacy

What is the typical proportion used for splitting data into training and test sets?

• 70% training and 30% testing

• 50% training and 50% testing

• 90% training and 10% testing

• 80% training and 20% testing

Which function is used to split data into training and test sets in the tutorial?

• random_split

• data_divide

• split_data

• train_test_split

In the context of the tutorial, what does the notation A/B represent?

• A function to merge data

• A method to split data

• A way to visualize data

• A technique to clean data

What is the purpose of using the 'count' function after splitting the data?

• To clean the data

• To merge the datasets

• To verify the number of rows in each dataset

• To visualize the data

How many rows are expected in the training dataset according to the tutorial?

• 50,000 rows

• 80,000 rows

• 20,000 rows

• 10,000 rows

What is the main goal of using the test dataset after training the model?

• To test the model's accuracy

• To train the model further

• To clean the data

• To visualize the data