DSE Supervised Learning: Classification 4

Question 1

Which of the following is the log-loss (cross-entropy loss) formula's primary purpose in classification?

Accepted Answer

Measure the difference between predicted probabilities and true class labels

Answer

Log-loss quantifies how well a classifier's predicted probabilities match the actual binary or multi-class labels.

Question 2

In gradient boosting for classification, what does each successive tree learn?

Accepted Answer

The residual errors (pseudo-residuals) of the previous ensemble

Answer

Each new tree in gradient boosting is fit to the negative gradient (pseudo-residuals) of the loss function from the current ensemble.

Question 3

What is the primary reason to use stratified k-fold cross-validation for classification?

Accepted Answer

It ensures each fold maintains the same class proportion as the full dataset

Answer

Stratified k-fold ensures each fold reflects the overall class distribution, giving a more reliable estimate of model performance, especially with imbalanced classes.

Question 4

Which regularization approach in logistic regression corresponds to an L1 penalty?

Accepted Answer

Lasso, which can shrink some coefficients to exactly zero

Answer

L1 regularization (Lasso) in logistic regression adds the absolute value of coefficients to the loss, promoting sparsity by zeroing out some weights.

Question 5

What does the F-beta score allow you to adjust compared to the standard F1 score?

Accepted Answer

The relative weight given to precision versus recall

Answer

The F-beta score uses a beta parameter to weight recall beta times more than precision, allowing domain-specific tuning of the precision-recall trade-off.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Supervised Learning: Classification 4