Data and Analytics Machine Learning Fundamentals 3

Question 1

Which metric is most appropriate when evaluating a classification model on a highly imbalanced dataset?

Accepted Answer

F1-score

Answer

F1-score balances precision and recall, making it a better metric than accuracy for imbalanced datasets where the majority class can dominate the accuracy score.

Question 2

What does an ROC curve illustrate in model evaluation?

Accepted Answer

The tradeoff between true positive rate and false positive rate at various thresholds

Answer

An ROC curve plots the true positive rate against the false positive rate across different classification thresholds, showing a model's discriminative ability.

Question 3

In k-fold cross-validation, what happens to the data?

Accepted Answer

The data is divided into k equal parts; each fold serves as the test set once while the rest train the model

Answer

K-fold cross-validation partitions data into k folds, cycling through each fold as the test set while training on the remaining k-1 folds, then averaging the results.

Question 4

What is a confusion matrix used to measure?

Accepted Answer

The counts of true positives, true negatives, false positives, and false negatives

Answer

A confusion matrix displays the counts of correct and incorrect predictions broken down by class, enabling calculation of precision, recall, and other classification metrics.

Question 5

Which of the following best describes hyperparameter tuning?

Accepted Answer

Searching for the best configuration settings that control the learning process

Answer

Hyperparameter tuning involves searching for optimal settings (like learning rate, tree depth, or regularization strength) that are set before training and control how the model learns.

Data and Analytics Practice Test

Data and Analytics Practice Test

Data and Analytics Machine Learning Fundamentals 3