You work for a real estate company where you are building a machine learning model to predict the prices of houses. You are using a regression decision tree. As you train your model, you see that it is overfitted to your training data, and it doesn’t generalize well to unseen data. How can you improve your situation and get better training results most efficiently?

Answer options:

A.Use a random forest by building multiple randomized decision trees and averaging their outputs to get the predictions of the housing prices.
B.Gather additional training data that gives a more diverse representation of the housing price data.
C.Use the “dropout” technique to penalize large weights and prevent overfitting.
D.Use feature selection to eliminate irrelevant features and iteratively train your model until you eliminate the overfitting.

Answer: A
Option A is correct because the random forest algorithm is well known to increase the prediction accuracy and prevent overfitting that occurs with a single decision tree. (See these articles comparing the decision tree and random forest algorithms: https://medium.com/datadriveninvestor/decision-tree-and-random-forest-e174686dd9eb and https://towardsdatascience.com/decision-trees-and-random-forests-df0c3123f991)
Option B is incorrect since gathering additional data will not necessarily improve the overfitting problem, especially if the additional data has the same noise level of the original data.
Option C is incorrect since while the “dropout” technique improves models that are overfitted, it is a technique used with neural networks, not decision trees.
Option D is incorrect since it requires significantly more effort than using the random forest algorithm approach.
Reference:
Please see this overview of the random forest machine learning algorithm:
https://medium.com/capital-one-tech/random-forest-algorithm-for-machine-learning-c4b2c8cc9feb