Recommender Systems: An Applied Approach using Deep Learning - Random Train-Test Split

The video tutorial explains how to perform a train-test split using TensorFlow. It begins with an introduction to the concept of train-test split and the importance of randomness. The instructor demonstrates setting a random seed for reproducibility and shuffling the dataset. The shuffled data is then split into training and testing sets, ensuring randomness to improve model results. The tutorial concludes with a brief mention of moving on to model development in the next video.

What is the primary reason for using a random split in train-test data division?

• To reduce the size of the dataset

• To improve prediction accuracy by ensuring randomness

• To make the data easier to visualize

• To ensure the data is sorted

Which function is used to set the random seed in TensorFlow?

• tf.seed.set_random

• tf.set_random_seed

• tf.random.set_seed

• tf.random.seed

What percentage of the data is used for the training set in this tutorial?

• 70%

• 60%

• 80%

• 50%

How is the test set created from the shuffled data?

• By taking the first 80% of the data

• By skipping the first 80% and taking the remaining 20%

• By taking the first 20% of the data

• By randomly selecting 20% of the data

What is the role of the 'int' function in the train-test split process?

• To shuffle the data

• To ensure the split indices are integers

• To sort the data

• To convert data to float

Why is it important to shuffle the data before splitting it into train and test sets?

• To make the data easier to read

• To ensure the data is in chronological order

• To make the dataset smaller

• To ensure randomness and improve model performance

What is the benefit of using a shuffled train-test split over a sequential one?

• It provides a more representative sample for training and testing

• It makes the data easier to visualize

• It reduces the dataset size

• It ensures the data is sorted