TensorFlow Random Forest

Install TensorFlow Decision Forests by running:

The critical difference between the random forest algorithm and decision tree is that decision trees are graphs that illustrate all possible outcomes of a decision using a branching approach. In contrast, the random forest algorithm output are a set of decision trees that work according to the output.

You can now use these models for classification, regression and ranking tasks – with the flexibility and composability of the TensorFlow and Keras. Random Forests are a popular type of decision forest model .27 May 2021

If the dataset has no many differentiations and we are new to decision tree algorithms, it is better to use Random Forest as it provides a visualized form of the data as well . If we want to explore more about decision trees and gradients, XGBoost is good option.