QUIZ 9 AI

When running our first decision tree, we took out "maxdepth=". This had the unfortunate result of...

Building a very large hard to understand tree

In order to interpret Decision Tree's it is necessary to first run a linear regression

False

Random Forests can only be used on classification problems.

False

One problem with decision trees is that they are prone to if you are not careful or do not set the ???? appropriately.

Max Depth

One problem with decision trees is that they are prone to ??? if you are not careful or do not set the

Overfitting

What is the first variable in a decision tree called (before any of the branches)?

Root

What is the terminal node as discussed in the lecture?

The last node (sometimes called a leaf if you google the term). The tree doesn't split after this.

Models, such as the random forest model we ran, often have a number of parameters that the analyst can choose or set. What is a the best source of up to date information about the different parameters that can be set?

The scikit learn documentation

Decision tree's are nice because they are fairly simple and straightforward to interpret.

True

The random forest algorithm prevents, or at least avoids to some extent, the problems with overfitting found in decision trees.

True

Random forests are [inputx] interpretable than decision trees

less