Train-Test Split Essentials

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Split data for evaluation.

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Suba K

India

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Train-Test Split Essentials


Froggy Jumps Train-Test Split EssentialsVersion en ligne Split data for evaluation. par Suba K 1 What is the main purpose of a train-test split in machine learning? a Speed up model training b Evaluate model performance on unseen data c Reduce the dataset size for faster results 2 What is a typical proportion for the test set in a common split? a 60-70% of the data b 20-30% of the data c 5-10% of the data 3 What does a stratified split ensure? a Removes noisy samples b Preserves the class distribution in both sets c Equals the training and test sizes exactly 4 Why should you shuffle data before a train-test split? a To reduce the dataset size b To avoid biased splits due to ordered data c To increase model complexity 5 What is the purpose of the random_state parameter? a Control model architecture b Provide a reproducible split by seeding the RNG c Determine the feature scaling method 6 How does train-test split differ from cross-validation? a CV uses a single split; train-test uses multiple b Split is done once; CV evaluates multiple folds c Train-test optimizes hyperparameters; CV trains only 7 When should you use a validation set in addition to train/test? a To replace the test set entirely b To tune hyperparameters without touching the test set c To increase training data 8 What is data leakage in the context of splits? a Randomly dropping samples b Overfitting due to too many features c Using information from the test set during training 9 Which function is commonly used to perform a train-test split in scikit-learn? a cross_val_score b train_test_split c GridSearchCV 10 What is stratified k-fold cross-validation commonly used for? a Maintaining class distribution across folds b Increasing training speed c Eliminating the need for a test split

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Train-Test Split Essentials

Froggy Jumps

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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Train-Test Split EssentialsVersion en ligne

par Suba K

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

What is a typical proportion for the test set in a common split?

What does a stratified split ensure?

Why should you shuffle data before a train-test split?

What is the purpose of the random_state parameter?

How does train-test split differ from cross-validation?

When should you use a validation set in addition to train/test?

What is data leakage in the context of splits?

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

What is stratified k-fold cross-validation commonly used for?