What is a key feature of Random Forests that enhances their robustness?

₹0.0

Question: What is a key feature of Random Forests that enhances their robustness?

Options:

Correct Answer: Bootstrap aggregating (bagging)

Solution:

Random Forests use bootstrap aggregating (bagging) to enhance robustness and reduce variance.

What is a key feature of Random Forests that enhances their robustness?

What is a key feature of Random Forests that enhances their robustness?

Step 1: Understand that Random Forests are a type of machine learning model used for classification and regression.
Step 2: Learn that they consist of many individual decision trees.
Step 3: Know that each decision tree is built using a random sample of the data.
Step 4: This random sampling is called 'bootstrap aggregating' or 'bagging'.
Step 5: Bagging helps to create diverse trees, which means they make different predictions.
Step 6: When you combine the predictions from all these trees, it reduces errors and makes the model more stable.
Step 7: This process of combining predictions from multiple trees enhances the robustness of the Random Forest model.

Bootstrap Aggregating (Bagging) – A technique used in Random Forests where multiple subsets of data are sampled with replacement to train individual trees, which helps in reducing overfitting and variance.