AML Model Evaluation & Optimization Techniques

Question 1

What is the purpose of a validation set in model evaluation?

Accepted Answer

To tune hyperparameters and assess model performance during training

Answer

To increase training accuracy.

Answer

To test the model’s final performance.

Answer

To train the model longer.

Question 2

Which metric is commonly used for binary classification performance?

Accepted Answer

Accuracy

Answer

Mean Absolute Error

Answer

Root Mean Squared Error

Answer

Silhouette Score

Question 3

What does cross-validation help to prevent?

Accepted Answer

Overfitting

Answer

Underfitting

Answer

Data normalization

Answer

Data leakage

Question 4

What is the ROC curve used for?

Accepted Answer

To analyze classification model performance

Answer

To assess regression performance.

Answer

To detect overfitting.

Answer

To perform feature scaling.

Question 5

Which metric evaluates regression model performance?

Accepted Answer

Mean Squared Error

Answer

F1-score

Answer

Precision

Answer

Confusion Matrix

Question 6

What does hyperparameter tuning aim to achieve?

Accepted Answer

Optimize model performance

Answer

Increase model bias.

Answer

Reduce model complexity.

Answer

Change the model architecture.

Question 7

Which technique prevents overfitting in optimization?

Accepted Answer

Regularization

Answer

Early stopping

Answer

Batch normalization

Answer

Feature selection

Question 8

Which method involves searching through a range of hyperparameters?

Accepted Answer

Grid search

Answer

Random sampling

Answer

Feature extraction

Answer

Model pruning

Question 9

Why is test data used in model evaluation?

Accepted Answer

To assess the final model's performance

Answer

To train the model faster.

Answer

To optimize hyperparameters.

Answer

To validate the training process.