Training Pipelines and Cross-Validation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data scientist is evaluating a model with 5-fold cross-validation. They notice that the standard deviation of the scores across folds is very low. What does this indicate?

   • AThe cross-validation was performed incorrectly.
   • BThe model is likely overfitting.
   • CThe model is underfitting.
   • DThe model's performance is stable across different data subsets.
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   Which scikit-learn class is used to chain together preprocessing and modeling steps into a single object that can be fit and used for predictions?

   • Across_val_score
   • BGridSearchCV
   • CStandardScaler
   • DPipeline
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4. 3application · easy

   A data scientist is running GridSearchCV with a parameter grid containing 2 values for C and 3 values for kernel in an SVM. They set cv=4. How many total model fits will GridSearchCV perform?

   • A8
   • B6
   • C12
   • D24
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5. 4application · medium

   A team is deciding between a single holdout validation set and 10-fold cross-validation for a large dataset of 5 million rows. Their main concern is getting a reliable performance estimate within a reasonable time budget. What is the primary trade-off they should consider?

   • AA single holdout set uses all data for training, while cross-validation wastes 10% of the data.
   • BCross-validation is always faster because it trains smaller models on fewer rows per fold.
   • CCross-validation uses data more efficiently but requires training 10 models instead of 1, increasing computational cost.
   • DA single holdout set always gives a more accurate estimate than cross-validation.
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   What does the cross_validate function in scikit-learn return that cross_val_score does not?

   • AA single average score
   • BThe best hyperparameters
   • CA dictionary containing fit and score times, and optionally train scores
   • DThe trained model object
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