Never in the same shape
CHAID (Chi-squared Automatic Interaction Detector) is an algorithm used for building decision trees, in particular for splitting variables based on their interactions with target variables. Unlike traditional decision trees, which rely primarily on the Gini index or entropy to choose splits, CHAID uses chi-square tests to automatically determine optimal splits.
A Decision Tree is a machine learning model that represents a series of logical decisions made based on attribute values. It is a tree structure which is used to make decisions or make predictions from the input data.
Random Forest is a machine learning algorithm that is based on building a set of decision trees. Each tree is built using a random subset of the training data and produces a prediction. Finally, the predictions from all the trees are combined through majority voting to get the final prediction.
The Gradient Boosting algorithm is a machine learning technique that builds a predictive model by combining several weaker models (usually decision trees) together into a single strong structure. The main goal of Gradient Boosting is to progressively improve the weaknesses of weak models, allowing you to create a stronger and more adaptable model.