Data Science with Python Certification Data Science with Python Model Evaluation and Validation 5

Question 1

Which technique can be used to visualize the tradeoff between precision and recall across different classification thresholds?

Accepted Answer

Precision-Recall curve

Answer

The Precision-Recall curve plots precision vs. recall at every possible threshold, especially useful for imbalanced datasets.

Question 2

What is the purpose of a learning curve in model evaluation?

Accepted Answer

To diagnose whether a model suffers from high bias or high variance

Answer

Learning curves plot training and validation scores as a function of training set size, revealing whether the bottleneck is bias (underfitting) or variance (overfitting).

Question 3

In time-series model evaluation, why is standard k-fold cross-validation inappropriate?

Accepted Answer

It allows future data to leak into training, violating temporal ordering

Answer

Standard k-fold randomly shuffles data, so validation folds may contain earlier timestamps than training folds, constituting look-ahead bias.

Question 4

What does sklearn.metrics.mean_absolute_percentage_error (MAPE) measure?

Accepted Answer

Average absolute percent deviation between predicted and actual values

Answer

MAPE computes the average of |actual - predicted| / |actual| * 100%, expressing error as a percentage of the true value.

Question 5

Which of the following statements about the R-squared (R²) metric is TRUE?

Accepted Answer

R² measures the proportion of variance in the target explained by the model

Answer

R² represents the fraction of target variance explained by the model, ranging from -∞ (worse than mean baseline) to 1.0 (perfect fit).

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Model Evaluation and Validation 5