In some classification contexts, it might be more of interest to obtain predicted probabilities of class membership rather than simply the labels themselves.
For any decision-tree based method, feature importance can be measured in a couple of ways.
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
A decision tree serves as the building block of most bagging and boosting algorithms and is always built using the concept of maximizing information.
Advantages: Ability to learn non-linear decision boundary, reduces overfitting. Disadvantages: Less interpretable than a single tree
There are three key parameters that can be tweaked: (a) Number of trees, (b) Number of Features and, (c) Sub sample size
Random forest is an ensemble learning model that uses multiple decision trees to make predictions. It can be used for both classification and regression tasks.
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