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A Random Forest is a very popular Machine Learning technique with Data Scientists.

It is an easy-to-interpret, stable technique that can be used for regression or classification tasks.

It therefore covers a large part of the problems of Machine Learning.

This algorithm will be based on trees called decision tree or decision tree.

The forest like the combination of trees, Random Forest is what is called an ensemble method that puts together or combines results from the different decision trees that compose it to obtain a great final result.

Random Forests can be composed of several tens or even hundreds of trees, the number of trees is a parameter that is generally adjusted by cross-validation.

Cross-validation is a technique for evaluating a Machine Learning algorithm consisting of training and testing the model on pieces of the starting dataset.

Each tree is trained on a subset of the dataset and yields a result.

The results of all the decision trees are then combined to give a final answer.

Each tree votes yes or no and the final answer is the one with the majority of votes.