Data Science Model Performance and Evaluation Questions and Answers

Question 1

A medical research team develops a model to screen for a rare but aggressive form of cancer.
The consequences of failing to identify a person who has the cancer (a false negative) are far more severe than mistakenly flagging a healthy person for additional testing (a false positive).
Which evaluation metric is most critical to maximize for this model?

Accepted Answer

Recall (Sensitivity)

Answer

Accuracy

Answer

Precision

Answer

Specificity

Question 2

Which of the following statements best describes a key difference between Mean Squared Error (MSE) and Mean Absolute Error (MAE) as regression model evaluation metrics?

Accepted Answer

MSE penalizes larger errors more heavily than MAE.

Answer

MAE is more sensitive to outliers than MSE.

Answer

MAE is always a smaller value than MSE for the same set of predictions.

Answer

MSE is easier to interpret because it is in the original units of the target variable.

Question 3

A data scientist has a very small dataset containing only 150 samples and needs to evaluate the performance of a predictive model. Which validation strategy would provide the most reliable estimate of the model's generalization ability?

Accepted Answer

K-Fold Cross-Validation.

Answer

A single 80/20 train-test split.

Answer

Training on 100% of the data and testing on the same data.

Answer

A single 50/50 train-test split.

Question 4

The Area Under the ROC Curve (AUC) is a popular metric for classification models. What does an AUC score of 0.85 signify?

Accepted Answer

There is an 85% probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative instance.

Answer

The model's accuracy is 85%.

Answer

The model correctly classifies 85% of the positive class instances.

Answer

The F1-Score of the model is 0.85.

Question 5

While training a deep learning model, a data scientist observes that the training accuracy steadily increases to 98%, while the validation accuracy increases for a while but then plateaus and starts to decrease, ending at 82%. This divergence in performance curves is a classic sign of what phenomenon?

Accepted Answer

Overfitting

Answer

Data leakage

Answer

Underfitting

Answer

High bias

Question 6

When performing hyperparameter tuning for a complex model with many hyperparameters, what is a primary advantage of using Random Search over Grid Search?

Accepted Answer

Random Search is generally more computationally efficient because it does not waste evaluations on unimportant hyperparameters.

Answer

Random Search is guaranteed to find the optimal hyperparameter combination.

Answer

Grid Search cannot be used for models with more than three hyperparameters.

Answer

Random Search explores every single possible combination of hyperparameter values.