Related Questions:
– What is Gradient Boosting (GBM)?
– How is Gradient Boosting different from Random Forest?
Gradient Boosting Machine (GBM) is a popular machine learning algorithm used for both classification and regression problems. GBM is an ensemble method that combines multiple weak learners to make a strong learner. The main advantages and disadvantages of a GBM model are as follows:
Advantages of GBM
[table id=1 /]
Disadvantages of GBM
[table id=3 /]
In summary, GBM is a powerful algorithm that can handle complex datasets and nonlinear relationships. However, it has some limitations, including overfitting, computational complexity, sensitivity to hyperparameters, and difficulty in interpretation.
Related Questions:
– What is Gradient Boosting (GBM)?
– How is Gradient Boosting different from Random Forest?
