Train/val/test split in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of splitting data into train, validation, and test sets?

Splitting data helps us train the model on one part (train), tune settings on another (validation), and finally check how well it works on new data (test). This avoids cheating and helps make better models.

beginner

In PyTorch, which function can help split datasets into train and validation sets?

You can use torch.utils.data.random_split() to split a dataset into parts like train and validation sets randomly.

beginner

Why should the test set be kept separate and only used once?

The test set shows how the model performs on new, unseen data. Using it only once prevents accidentally tuning the model to the test data, which would give a false sense of accuracy.

beginner

What typical proportions are used for train, validation, and test splits?

Common splits are 70-80% for training, 10-15% for validation, and 10-15% for testing. These can vary depending on data size and problem.

beginner

How does validation data help during model training?

Validation data helps check the model's performance during training to adjust settings like learning rate or stop training early to avoid overfitting.

What is the main use of the validation set?

A To collect more data

B To train the model

C To test the final model performance

D To tune model settings during training

The validation set is used to tune model settings like hyperparameters during training.

Which PyTorch function helps split datasets randomly?

A torch.split()

B torch.utils.data.random_split()

C torch.chunk()

D torch.data.split_dataset()

torch.utils.data.random_split() splits datasets randomly into parts.

Why should the test set be used only once?

A To avoid overfitting to test data

B To speed up training

C To save memory

D To increase training data size

Using the test set only once prevents tuning the model to test data, ensuring honest evaluation.

A common split for train/validation/test is:

A 80% train, 10% val, 10% test

B 90% train, 5% val, 5% test

C 30% train, 30% val, 40% test

D 50% train, 25% val, 25% test

80% train, 10% validation, and 10% test is a common and balanced split.

What happens if you train and test on the same data?

A Training is faster

B Model performs well on new data

C Model may overfit and perform poorly on new data

D Model accuracy decreases on training data

Training and testing on the same data causes overfitting, so the model may fail on new data.

Explain why we split data into train, validation, and test sets and how each is used.

Think about how to avoid cheating and improve model reliability.

Describe how to split a PyTorch dataset into train and validation sets and why random splitting is useful.

Consider how to fairly divide data for training and tuning.