In Gradient Boosted Trees, the term “Gradient” refers to the gradient of the loss function being optimized. The negative gradient indicates the direction of steepest descent, which is the direction that will result in the greatest decrease in loss function.
XGBoost, or Extreme Gradient Boosting, is an open-source implementation of GBM. XGBoost has become very popular for its scalability, accuracy, and speed
AdaBoost and Gradient Boosting are both ensemble learning techniques, but they differ in their approach to building the ensemble and updating the weights
A weak learner is an algorithm that performs only slightly better than random guessing on a given problem. A strong learner can learn complex patterns in the data
For any decision-tree based method, feature importance can be measured in a couple of ways.
Tuning the combination of number of trees and learning rate is a good way to ensure you are creating a model with appropriate complexity.
Use GBM when dataset is noisy, highly unbalanced data, model performance is the priority. Use Random Forest when dealing with missing values,
Both GBM and Random Forest are ensemble methods, however, they differ in their training process, feature selection, overfitting, and interpretability. RF provides a clear measure of feature importance, while GBM can achieve higher accuracy
Advantages: GBM is a powerful algorithm that can handle complex datasets and nonlinear relationships. Disadvantages:overfitting, computational complexity
Key hyperparameters for a GBM model are: (a) Learning Rate (b) No. of tree (c) Depth of the tree (d) Number of features at each split
Partner Ad