Supervised Learning: Classification Flashcards
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Which of the following is the log-loss (cross-entropy loss) formula's primary purpose in classification?
Answer: Measure the difference between predicted probabilities and true class labels
Log-loss quantifies how well a classifier's predicted probabilities match the actual binary or multi-class labels.
In gradient boosting for classification, what does each successive tree learn?
Answer: The residual errors (pseudo-residuals) of the previous ensemble
Each new tree in gradient boosting is fit to the negative gradient (pseudo-residuals) of the loss function from the current ensemble.
What is the primary reason to use stratified k-fold cross-validation for classification?
Answer: It ensures each fold maintains the same class proportion as the full dataset
Stratified k-fold ensures each fold reflects the overall class distribution, giving a more reliable estimate of model performance, especially with imbalanced classes.
Which regularization approach in logistic regression corresponds to an L1 penalty?
Answer: Lasso, which can shrink some coefficients to exactly zero
L1 regularization (Lasso) in logistic regression adds the absolute value of coefficients to the loss, promoting sparsity by zeroing out some weights.
What does the F-beta score allow you to adjust compared to the standard F1 score?
Answer: The relative weight given to precision versus recall
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.
Which of the following describes a 'discriminative' classification model?
Answer: It models the conditional probability P(Y|X) directly
Discriminative models like logistic regression and SVMs learn the decision boundary by modeling P(Y|X) directly, without modeling how features are generated.
When would you prefer a Decision Tree over Logistic Regression for classification?
Answer: When the data has complex nonlinear feature interactions and interpretability is needed
Decision trees naturally capture nonlinear interactions without feature engineering, and their structure is human-interpretable via tree visualization.