Machine Learning Fundamentals Flashcards
6 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Machine Learning Fundamentals flashcards as text
Which type of machine learning uses labeled training data to learn a mapping from inputs to outputs?
Answer: Supervised learning
Supervised learning trains on labeled input-output pairs to learn a predictive mapping.
What is the term for a model that performs well on training data but poorly on new data?
Answer: Overfitting
Overfitting occurs when a model memorizes training data instead of learning generalizable patterns.
Which algorithm builds an ensemble of decision trees to improve prediction accuracy?
Answer: Random forest
Random forest combines many decision trees, using bagging and feature randomness to reduce variance.
What does the 'bias-variance tradeoff' describe in machine learning?
Answer: The balance between underfitting and overfitting
The bias-variance tradeoff describes the tension between a model's error from wrong assumptions (bias) and sensitivity to fluctuations in training data (variance).
Which metric is most appropriate when false negatives are more costly than false positives?
Answer: Recall
Recall (sensitivity) measures the proportion of actual positives correctly identified, minimizing false negatives.
What is k-fold cross-validation used for in machine learning?
Answer: Estimating model performance on unseen data
K-fold cross-validation splits data into k folds, training and validating k times to get a reliable performance estimate.