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

F1-score balances precision and recall, making it far more informative than accuracy when one class heavily dominates.

Question 2

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

Accepted Answer

StratifiedKFold

Answer

StratifiedKFold ensures each fold maintains the same class distribution as the overall dataset.

Question 3

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

Accepted Answer

Performance equivalent to random guessing

Answer

An AUC of 0.5 means the model has no discriminative ability and performs no better than a random classifier.

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.confusion_matrix(y_true, y_pred) returns the matrix of true vs. predicted class counts.

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

refit=True causes GridSearchCV to retrain the best-found model on the complete training data, making it ready for prediction.

Data Science with Python Certification Practice Test