Supervised Learning: Classification Flashcards

Which metric is most appropriate when false negatives are far more costly than false positives, such as in cancer screening?

Answer: Recall

Recall (sensitivity) measures how many actual positives are correctly identified, minimizing false negatives.

In a Random Forest classifier, how does increasing the number of trees typically affect variance?

Answer: Decreases variance

More trees in a Random Forest reduce variance by averaging more independent predictions, though with diminishing returns.

What is the primary difference between hard and soft voting in ensemble classifiers?

Answer: Soft voting averages predicted probabilities; hard voting uses majority class labels

Soft voting averages the class probabilities from each classifier, while hard voting picks the class predicted most frequently.

Which kernel function in an SVM maps data into infinite-dimensional space?

Answer: RBF (Gaussian) kernel

The RBF kernel implicitly maps data into an infinite-dimensional feature space via the Gaussian function.

A logistic regression model outputs a probability of 0.45 for class 1. With a default threshold of 0.5, what class is predicted?

Answer: Class 0

Since 0.45 is below the 0.5 threshold, the model predicts class 0.

What does the term 'class imbalance' refer to in classification?

Answer: One class has significantly more samples than other classes in the training data

Class imbalance occurs when the distribution of target classes in the dataset is skewed, with one class having far more samples.

Which technique specifically addresses class imbalance by generating synthetic samples for the minority class?

Answer: SMOTE

SMOTE (Synthetic Minority Over-sampling Technique) creates new synthetic samples by interpolating between existing minority class examples.