Machine Learning & Predictive Analytics Flashcards

A model achieves 99% accuracy on training data but only 62% on test data. What is the most likely problem?

Answer: Overfitting

A large gap between high training accuracy and low test accuracy is the classic signature of overfitting.

Which technique is most appropriate for reducing overfitting in a decision tree?

Answer: Pruning the tree

Pruning removes branches that add little predictive power, reducing complexity and overfitting.

In a confusion matrix for a fraud detection model, what does a false negative represent?

Answer: A fraudulent transaction missed by the model

A false negative is an actual positive (fraud) that the model incorrectly labeled as negative (legitimate).

Which metric is best when the cost of missing positive cases is very high, such as disease screening?

Answer: Recall

Recall measures the proportion of actual positives caught, which matters most when missing them is costly.

What is the primary purpose of feature scaling before applying k-nearest neighbors?

Answer: To ensure all features contribute equally to distance calculations

KNN relies on distance, so features on larger scales would otherwise dominate the calculation.

A regression model has a high R-squared but residuals show a clear curved pattern. What does this indicate?

Answer: A nonlinear relationship is not being captured

Systematic curvature in residuals signals that a linear model is missing a nonlinear relationship.

Which algorithm is an example of an ensemble method that combines many weak learners sequentially?

Answer: Gradient boosting

Gradient boosting builds trees sequentially, each correcting errors of the previous ones.