Explain all of decision tree and random forest?

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Accepted Answer

Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.

Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.
Random forest is a collection of decision trees trained on random subsets of the data, and the final prediction is made by averaging the predictions of all the individual trees.
Decision trees are prone to overfitting, while random forests help reduce overfitting by combining multiple trees.
Random forests are more accurate and robust compared to individual decision trees.
Example: Decision tree can be used to predict whether a customer will buy a product based on their age, income, and purchase history. Random forest can be used for image classification by combining multiple decision trees trained on different subsets of images.

Explain all of decision tree and random forest?

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