Supervised Learning Flashcards
7 cards from real Machine Learning 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 flashcards as text
Which loss function is typically used for training a binary classification model with logistic regression?
Answer: Binary Cross-Entropy
Binary cross-entropy (log loss) measures the divergence between predicted probabilities and true binary labels, making it ideal for logistic regression.
What is the main difference between bagging and boosting ensemble methods?
Answer: Bagging trains models in parallel on random subsets; boosting trains sequentially focusing on errors
Bagging (e.g., Random Forest) trains models in parallel on bootstrap samples to reduce variance, while boosting trains models sequentially to reduce bias by focusing on previous errors.
In a confusion matrix for binary classification, what does a 'False Positive' represent?
Answer: A negative instance incorrectly classified as positive
A false positive (Type I error) occurs when the model predicts positive but the true label is negative.
Which regularization method can perform automatic feature selection by driving some coefficients exactly to zero?
Answer: L1 (Lasso)
L1 (Lasso) regularization uses the absolute value of coefficients in its penalty, which tends to produce sparse solutions with some weights exactly zero.
What is 'feature scaling' and why is it important for algorithms like SVMs and k-NN?
Answer: Normalizing feature ranges so no single feature dominates distance calculations
Feature scaling (e.g., standardization or min-max normalization) ensures all features contribute equally to distance-based calculations, which is critical for SVMs and k-NN.
What does the ROC curve plot in a binary classification problem?
Answer: True Positive Rate vs. False Positive Rate at various thresholds
The ROC curve plots the True Positive Rate (sensitivity) against the False Positive Rate (1-specificity) at various classification thresholds.
Which of the following is an example of a regression problem in supervised learning?
Answer: Predicting the selling price of a house based on its features
Predicting house prices produces a continuous numeric output, making it a regression problem rather than a classification problem.
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