AdaBoost, Gradient Boosting, XG Boost:: Similarities & Differences

Here are some similarities and differences between Gradient Boosting, XGBoost, and AdaBoost:

Similarities:

1. All three algorithms are ensemble methods that use decision trees (or other weak learners) as base models to make predictions.
2. All three algorithms involve building a sequence of models, where each subsequent model tries to correct the errors made by the previous model.
3. All three algorithms can handle a mixture of numerical and categorical features and can capture complex non-linear relationships between features and target variables.
4. All three algorithms can be used for both regression and classification problems.
5. All three algorithms require careful tuning of hyperparameters to prevent overfitting and improve the performance of the model.

Differences:

1. Methodology: Gradient Boosting and XGBoost use a similar approach to build the sequence of models, where each subsequent model tries to minimize the error of the previous model. In contrast, AdaBoost uses a different approach, where each subsequent model tries to focus on the samples that were misclassified by the previous model.
2. Regularization: XGBoost includes additional…