What are decision Trees and All the algorithms that you have used in ur project?

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Accepted Answer

Decision Trees are a type of supervised learning algorithm used for classification and regression tasks.

Decision Trees are used to create a model that predicts the value of a target variable based on several input variables.
The algorithm splits the data into subsets based on the most significant attribute and continues recursively until a leaf node is reached.
Some of the algorithms used in my project include Random Forest, Gradient Boosting, and XGBoost.
Random Forest is an ensemble learning method that constructs multiple decision trees and combines their outputs.
Gradient Boosting is a boosting algorithm that builds decision trees in a sequential manner, with each tree correcting the errors of the previous one.
XGBoost is an optimized implementation of Gradient Boosting that uses a more efficient tree construction algorithm.

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