Train-Test Split Mastery

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ML split and evaluation basics

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Suba K

India

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Train-Test Split Mastery


Train-Test Split MasteryOnline version ML split and evaluation basics by Suba K 1 What is the primary purpose of a train-test split in machine learning? a To increase training data size b To evaluate model performance on unseen data c To tune the model automatically d To reduce computation time during training 2 Which parameter ensures reproducibility of the split by fixing the randomness seed? a test_size b random_state c shuffle d stratify 3 Which argument keeps the same class distribution in both train and test sets? a stratify b random_state c shuffle d train_size 4 If test_size=0.2, what fraction of data goes to the test set? a Ten percent b 20% c 2% d 50% 5 For time-series data, what split practice should be avoided? a Using random_state b Stratifying by label c Shuffling the data d Small test_size 6 What does a stratified split preserve in classification tasks? a Exact same samples in both sets b Order of samples c Proportions of each class in both sets d Average feature values 7 Which function in scikit-learn is commonly used to perform train/test split? a split_data b holdout_split c fit_resample d train_test_split 8 What technique is typically used to obtain more robust performance estimates beyond a single train/test split? a Hyperparameter tuning b One-hot encoding c Feature scaling d Cross-validation 9 Which split strategy is preferred for imbalanced datasets to maintain class ratios? a Time-based split b Stratified split c Shuffled split d Random split 10 What is data leakage in the context of train/test splitting? a Using future data only in test b All features omitted c Test data being larger than train d Test data influencing model during training

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Train-Test Split Mastery

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About this activity

Created by

Download the paper version to play

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Train-Test Split MasteryOnline version

by Suba K

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

Which parameter ensures reproducibility of the split by fixing the randomness seed?

Which argument keeps the same class distribution in both train and test sets?

If test_size=0.2, what fraction of data goes to the test set?

For time-series data, what split practice should be avoided?

What does a stratified split preserve in classification tasks?

Which function in scikit-learn is commonly used to perform train/test split?

What technique is typically used to obtain more robust performance estimates beyond a single train/test split?

Which split strategy is preferred for imbalanced datasets to maintain class ratios?

What is data leakage in the context of train/test splitting?