Quick Check 20.4 – Data 100, Summer 2020

JavaScript isn't enabled in your browser, so this file can't be opened. Enable and reload.

The complexity of decision trees limits their use in many situations. However, modifications can be implemented to mitigate this. Which of the following are reasonable examples of such modifications? *

Restricting the depth

Not splitting nodes containing small fractions of the observations

Increasing the size of the training set

Pruning the nodes of a fully grown tree

Required

To train a Random Forest regression model, fit a ___ to each of several ___ and select features at random for consideration at each node. *

linear regression model, randomly sampled training sets

regression tree, randomly sampled training sets

linear regression model, bootstrap samples of the training set

regression tree, bootstrap samples of the training set

The main idea behind ensemble prediction methods such as Random Forests is that aggregating predictions from predictors applied to multiple bootstrap samples of the training set: *

reduces bias

reduces model variance

reduces both bias and model variance

none of these

Submit

Clear form

This form was created inside of UC Berkeley. Report Abuse

Forms