Data Science Supervised Learning Algorithms 3

Question 1

In random forests, what technique reduces correlation between individual trees?

Accepted Answer

Random feature subsampling at each split

Answer

Selecting a random subset of features at each split decorrelates the trees in a random forest.

Question 2

What is the key difference between bagging and boosting?

Accepted Answer

Bagging trains models independently in parallel; boosting trains sequentially focusing on errors

Answer

Bagging builds independent models in parallel while boosting builds models sequentially to correct prior errors.

Question 3

In gradient boosting, what do successive trees primarily fit?

Accepted Answer

The residual errors of previous trees

Answer

Each new tree in gradient boosting fits the residuals (errors) left by the prior ensemble.

Question 4

What does a large C value in an SVM control?

Accepted Answer

A narrower margin penalizing misclassifications more

Answer

A large C penalizes misclassifications heavily, producing a narrower margin and less regularization.

Question 5

Which loss function is typically minimized in standard linear regression?

Accepted Answer

Mean squared error

Answer

Ordinary least squares linear regression minimizes the mean squared error between predictions and targets.

Data Science Practice Test