Knowledge Flashcards

What distinguishes supervised learning from unsupervised learning?

Answer: Supervised learning trains on labeled input-output pairs; unsupervised learning finds structure in unlabeled data

Supervised learning maps inputs to known outputs using labeled examples, while unsupervised learning discovers hidden patterns in data without labels.

Which metric is most appropriate for evaluating a classifier on a heavily imbalanced dataset?

Answer: F1-Score or AUC-ROC

F1-Score and AUC-ROC account for class imbalance, whereas accuracy can be misleadingly high when the majority class dominates.

In gradient descent, what does the learning rate control?

Answer: The step size taken in the direction of the negative gradient at each update

The learning rate scales the gradient update step; too large causes divergence, too small causes slow convergence.

What type of relationship does Pearson correlation measure?

Answer: Linear relationship between two continuous variables

Pearson correlation measures the strength and direction of the linear association between two continuous variables, ranging from -1 to +1.

Which of the following is a key assumption of linear regression?

Answer: There is a linear relationship between predictors and the response variable

Linear regression assumes the expected value of the outcome is a linear combination of the predictor variables.

What is the role of a validation set during model training?

Answer: It provides unbiased data for tuning hyperparameters without contaminating the test set

The validation set allows iterative hyperparameter tuning while keeping the test set pristine for final unbiased evaluation.

Which ensemble method trains multiple models sequentially, with each model focusing on examples the previous one misclassified?

Answer: Boosting

Boosting trains models sequentially, re-weighting misclassified examples so subsequent learners correct earlier mistakes.