Deep Learning - Crash Course 2023 - Train Test Split

The video tutorial explains the importance of splitting data into training and test sets in machine learning. It demonstrates how to use the scikit-learn library to perform a train-test split, initially in a 75-25 ratio, and then adjusts for deep learning with a 90-10 ratio. The tutorial highlights the significance of using the 'stratify' parameter to ensure balanced data distribution and the 'random state' parameter for reproducibility. The video concludes by emphasizing the need for consistent data splits to achieve reliable model accuracy.

Why is it important to split data into training and test sets in machine learning?

• To make the data more complex

• To increase the speed of data processing

• To ensure the model is tested on unseen data

• To reduce the size of the dataset

What is the default ratio for splitting data into training and test sets using scikit-learn?

• 50-50

• 80-20

• 60-40

• 75-25

In deep learning, what is the preferred ratio for splitting data into training and test sets?

• 95-5

• 90-10

• 80-20

• 70-30

What parameter is used to specify the proportion of data to be used as test data in scikit-learn?

• split_ratio

• data_fraction

• test_size

• train_size

Why is stratification important when splitting data?

• To ensure equal distribution of classes in both sets

• To increase the size of the training set

• To make the test set more challenging

• To reduce the complexity of the model

What is the purpose of setting a 'random_state' in data splitting?

• To randomize the data completely

• To ensure reproducibility of results

• To increase the randomness of the split

• To decrease the size of the dataset

Which parameter helps in ensuring that the data split is reproducible?

• shuffle

• test_size

• random_state

• stratify