8. Explain Random Forest and Decision Tree?

Question

Accepted Answer

Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy.

Random Forest is a type of supervised learning algorithm used for classification and regression tasks.
It creates multiple decision trees and combines their outputs to make a final prediction.
Each decision tree is built using a random subset of features and data points to reduce overfitting.
Random Forest is more accurate than a single decision tree and less prone to overfitting.
Decision Tree is a tree-like model that makes decisions based on a set of rules.
It is a type of supervised learning algorithm used for classification and regression tasks.
It splits the data into smaller subsets based on the most significant feature and creates a tree-like model.
Each internal node represents a test on a feature, each branch represents the outcome of the test, and each leaf node represents a class label or a numerical value.
Decision Tree is easy to interpret and visualize, but prone to overfitting if not pruned properly.

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