Data Science Supervised Learning Models 3

Question 1

What is the bias-variance tradeoff?

Accepted Answer

A balance between underfitting and overfitting errors

Answer

High bias underfits while high variance overfits; the tradeoff seeks minimal total error.

Question 2

In k-nearest neighbors, what happens as k increases?

Accepted Answer

The decision boundary becomes smoother

Answer

Larger k averages over more neighbors, smoothing the boundary and reducing variance.

Question 3

Which ensemble method builds trees sequentially, each correcting the previous one's errors?

Accepted Answer

Gradient boosting

Answer

Gradient boosting fits each new tree to the residual errors of the prior ensemble.

Question 4

What does a high recall but low precision indicate?

Accepted Answer

Many false positives but few false negatives

Answer

High recall catches most positives, but low precision means many predicted positives are wrong.

Question 5

Why is feature scaling important for SVM and KNN?

Accepted Answer

They rely on distance calculations sensitive to feature magnitude

Answer

Distance-based models let large-magnitude features dominate unless features are scaled.

Data Science Practice Test