Improve Your Regression Decision Tree Model for Better Predictions
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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-c4b2c8cc9febThe correct answer is A: Use a random forest by building multiple randomized decision trees and averaging their outputs to get the predictions of the housing prices.
Explanation:
Overfitting occurs when a machine learning model learns the training data too well, resulting in poor performance on new data. When a model overfits, it becomes too complex and memorizes the training data instead of learning from it. This leads to a high variance in the model, causing it to perform poorly on new data.
To address the overfitting problem, we need to reduce the variance in the model. Here are the explanations for why the other options are not the most efficient solution:
B. Gathering more data can help reduce overfitting, but it is not always the most efficient solution, especially when the amount of data is limited.
C. Dropout is a regularization technique that can help to reduce overfitting in neural networks. However, the question mentions that a regression decision tree is being used, not a neural network, so this technique is not applicable in this situation.
D. Feature selection can also help reduce overfitting by removing irrelevant features. However, the question doesn't mention any specific features that are causing overfitting, so this solution is not the most efficient one. Additionally, iteratively training the model after each feature selection can be time-consuming and computationally expensive.
A. A random forest is an ensemble learning technique that builds multiple randomized decision trees and averages their outputs to get the predictions. This approach can help reduce overfitting by introducing randomness into the model. Randomness is introduced by selecting a random subset of features and data samples for each tree. By combining the results of multiple trees, the model can generalize better to new data.
In summary, the most efficient solution to address overfitting in a regression decision tree is to use a random forest.
