ML Train-Test Split

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ML split basics

Created by

Suba K

India

Download the paper version to play

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Froggy Jumps ML Train-Test SplitOnline version ML split basics by Suba K 1 What is the primary purpose of a train-test split in machine learning? a Reduce training time b Train the model with all data c Evaluate model performance on unseen data 2 A common split ratio for train-test is: a 100% train, 0% test b 80% train, 20% test c 50% train, 50% test 3 What does stratified sampling ensure in a split? a Class proportions stay similar in train and test b All recent data is used c Faster computation 4 Data leakage occurs when you: a Use information from the test set to train b Use a fixed random seed c Shuffle the data before split 5 What is cross-validation? a Training only on validation set b A method using multiple train/test splits to evaluate models c A single train-test split 6 What does setting a random_state achieve? a Faster training b Higher accuracy c Reproducible split results 7 Why should you not use only the training data for evaluation? a Because it increases training time b Because it overestimates performance on new data c Because it looks at the test set too much 8 In ML, what is the validation set typically used for? a Training the model from scratch b Final model evaluation on unseen data c Hyperparameter tuning and model selection 9 Should data be shuffled before splitting, and why? a No, shuffling harms performance b Only shuffle after training c Yes, to avoid order bias in the split 10 What is k-fold cross-validation mainly used for? a Reducing data size b Estimating model performance more reliably across splits c Replacing the test set entirely

Froggy Jumps

ML Train-Test SplitOnline version

ML split basics

by Suba K

What is the primary purpose of a train-test split in machine learning?

Reduce training time

Train the model with all data

Evaluate model performance on unseen data

A common split ratio for train-test is:

100% train, 0% test

80% train, 20% test

50% train, 50% test

What does stratified sampling ensure in a split?

Class proportions stay similar in train and test

All recent data is used

Faster computation

Data leakage occurs when you:

Use information from the test set to train

Use a fixed random seed

Shuffle the data before split

What is cross-validation?

Training only on validation set

A method using multiple train/test splits to evaluate models

A single train-test split

What does setting a random_state achieve?

Faster training

Higher accuracy

Reproducible split results

Why should you not use only the training data for evaluation?

Because it increases training time

Because it overestimates performance on new data

Because it looks at the test set too much

In ML, what is the validation set typically used for?

Training the model from scratch

Final model evaluation on unseen data

Hyperparameter tuning and model selection

Should data be shuffled before splitting, and why?

No, shuffling harms performance

Only shuffle after training

Yes, to avoid order bias in the split

What is k-fold cross-validation mainly used for?

Reducing data size

Estimating model performance more reliably across splits

Replacing the test set entirely

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ML Train-Test Split

Froggy Jumps

About this activity

Created by

Download the paper version to play

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Froggy Jumps

ML Train-Test SplitOnline version

by Suba K

What is the primary purpose of a train-test split in machine learning?

A common split ratio for train-test is:

What does stratified sampling ensure in a split?

Data leakage occurs when you:

What is cross-validation?

What does setting a random_state achieve?

Why should you not use only the training data for evaluation?

In ML, what is the validation set typically used for?

Should data be shuffled before splitting, and why?

What is k-fold cross-validation mainly used for?

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