Supervised Learning Models Flashcards
7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Supervised Learning Models flashcards as text
In a logistic regression model, what does the sigmoid function output represent?
Answer: A probability between 0 and 1
The sigmoid squashes the linear combination into a value between 0 and 1 interpreted as a probability.
Which loss function is most appropriate for a binary classification task?
Answer: Binary cross-entropy
Binary cross-entropy penalizes confident wrong predictions and suits probabilistic binary outputs.
What is the primary purpose of regularization (L1/L2) in supervised models?
Answer: Reduce overfitting by penalizing large weights
Regularization adds a penalty on coefficient magnitude to discourage overly complex models that overfit.
Which model uses a margin-maximizing hyperplane to separate classes?
Answer: Support Vector Machine
SVMs find the hyperplane that maximizes the margin between the nearest points of each class.
In a decision tree, what does the Gini impurity measure?
Answer: Probability of misclassifying a randomly chosen sample
Gini impurity quantifies how often a randomly labeled sample would be misclassified at a node.
What distinguishes a supervised model from an unsupervised one?
Answer: It trains on labeled target values
Supervised learning uses input-output pairs with known labels to learn a mapping.
Which metric is best for evaluating a classifier on an imbalanced dataset?
Answer: F1 score
The F1 score balances precision and recall, making it robust when classes are imbalanced.