Data Science with Python Certification Data Science with Python Model Evaluation and Validation 2

Question 1

Which metric is most appropriate for evaluating a model on a severely imbalanced binary classification dataset?

Accepted Answer

F1-score

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

Question 2

In scikit-learn, which cross-validation strategy preserves the percentage of samples for each class label?

Accepted Answer

StratifiedKFold

Answer

KFold

Answer

ShuffleSplit

Answer

LeaveOneOut

Question 3

What does a ROC AUC score of 0.5 indicate about a binary classifier?

Accepted Answer

Performance equivalent to random guessing

Answer

Perfect classification

Answer

Better than random chance

Answer

Complete misclassification

Question 4

Which Python function from sklearn.metrics computes the confusion matrix for a classification model?

Accepted Answer

sklearn.metrics.confusion_matrix()

Answer

sklearn.metrics.classification_report()

Answer

sklearn.metrics.roc_auc_score()

Answer

sklearn.metrics.accuracy_score()

Question 5

When using GridSearchCV in scikit-learn, what does the refit=True parameter do?

Accepted Answer

Refits the best estimator on the entire training set after search

Answer

Refits the model on each fold separately

Answer

Runs cross-validation twice

Answer

Saves all fitted models to disk

Question 6

Which of the following best describes the purpose of a validation set (as distinct from a test set)?

Accepted Answer

Data used to tune hyperparameters during development

Answer

Final unbiased estimate of model performance

Answer

Data used to train model weights

Answer

Data used for feature engineering only

Question 7

What is the primary risk of performing feature selection on the entire dataset before applying cross-validation?

Accepted Answer

Data leakage leading to overly optimistic performance estimates

Answer

Underfitting the model

Answer

Slower training time

Answer

Increased model variance

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