Föreläsning 5 (Regression)

Question 1

Are random forests an example of an ensamble method?

Answer 1

Yes!

Question 2

How do you introduce variation in random forests?

Answer 2

by (i) varying the dataset
slightly and (ii) restricting the splits
allowed at each level

Question 3

What is bootstrapping?

Answer 3

Bootstrapping in AI refers to the process of building and improving machine learning models by using their own predictions as training data. In other words, the model is initially trained on a small set of labeled data and then used to make predictions on new, unlabeled data. The predictions are then added to the labeled dataset, and the model is retrained on the larger dataset.

Question 4

Name 2 advantages of using random forests

Answer 4

• Random forests is considered a highly accurate and robust method
  because of the number of decision trees participating in the process.
• It does not suffer from the overfitting problem. The main reason is
  that it takes the average of all the predictions, which cancels out the
  biases.

Question 5

Name 2 disadvantages of using random forests

Answer 5

• Random forests is slow in generating predictions because it has
  multiple decision trees. This whole process is time-consuming.
• The model is difficult to interpret compared to a decision tree, where
  you can easily make a decision by following the path in the tree.

Question 6

What is regression?

Answer 6

Mapping data points to numbers