Machine Learning Model Evaluation 3

Question 1

Which metric is best suited for evaluating regression models?

Accepted Answer

Mean Squared Error (MSE)

Answer

MSE measures the average squared difference between predicted and actual continuous values, making it standard for regression evaluation.

Question 2

What is the key advantage of using Mean Absolute Error (MAE) over Mean Squared Error (MSE)?

Accepted Answer

MAE is less sensitive to outliers

Answer

MAE uses absolute differences rather than squared differences, making it more robust to outliers compared to MSE.

Question 3

What does the R² (coefficient of determination) value of 0 indicate?

Accepted Answer

The model performs as well as a constant mean predictor

Answer

R²=0 means the model explains none of the variance in the target variable, performing no better than simply predicting the mean.

Question 4

What is 'data leakage' in the context of model evaluation?

Accepted Answer

Test set information influencing the training process

Answer

Data leakage occurs when information from the test/future data leaks into training, causing overly optimistic evaluation metrics.

Question 5

Which technique helps evaluate model stability across multiple different random train/test splits?

Accepted Answer

Repeated cross-validation

Answer

Repeated cross-validation runs k-fold CV multiple times with different random splits to assess how stable performance estimates are.

Machine Learning Practice Test