ML Train-Test Split

Froggy Jumps

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ML split basics

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Suba K

India

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ML Train-Test Split


Froggy Jumps ML Train-Test SplitVersion en ligne ML split basics par 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

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ML Train-Test Split

Froggy Jumps

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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ML Train-Test SplitVersion en ligne

par 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?