XGBoost Classifier

The XGBoost Classifier is a powerful and efficient gradient boosting model from the XGBoost library. It combines multiple weak learners, typically decision trees, to create a strong ensemble model. In each iteration, a new weak learner is added to the ensemble, focusing on correcting the errors made by the previous learners. Predictions from the ensemble model are given by a weighted sum of the predictions of the individual weak learners. XGBoost Classifier is known for its high performance, scalability, and ability to handle imbalanced datasets, making it a popular choice for a wide range of machine learning tasks.

Advantages:

• It requires less feature engineering
• Feature importance can be found out
• It is robust to outliers
• It performs well on large sized datasets
• It is computational fast

Disadvantages:

• It has the problem of overfitting
• It is harder to tune as there are too many hyperparameters