How Random Forest Classification works

Random Forest Classification is an ensemble learning technique designed to enhance the accuracy and robustness of classification tasks. The algorithm builds a multitude of decision trees during training and outputs the class that is the mode of the classification classes. Each decision tree in the random forest is constructed using a subset of the training data and a random subset of features introducing diversity among the trees, making the model more robust and less prone to overfitting.

The random forest algorithm employs a technique called bagging (Bootstrap Aggregating) to create these diverse subsets.

During the training phase, each tree is built by recursively partitioning the data based on the features. At each split, the algorithm selects the best feature from the random subset, optimizing for information gain or Gini impurity. The process continues until a predefined stopping criterion is met, such as reaching a maximum depth or having a minimum number of samples in each leaf node.

Once the random forest is trained, it can make predictions, using each tree “votes” for a class, and the class with the most votes becomes the predicted class for the input data.

Feature Selection in Random Forests

Feature selection in Random Forests is inherently embedded in the construction of individual decision trees and the aggregation process.

During the training phase, each decision tree is built using a random subset of features, contributing to diversity among the trees. The process is, known as feature bagging, helps prevent the dominance of any single feature and promotes a more robust model.

The algorithm evaluates various subsets of features at each split point, selecting the best feature for node splitting based on criteria such as information gain or Gini impurity. Consequently, Random Forests naturally incorporate a form of feature selection, ensuring that the ensemble benefits from a diverse set of features to enhance generalization and reduce overfitting.

In this article, we will see how to build a Random Forest Classifier using the Scikit-Learn library of Python programming language and to do this, we use the IRIS dataset which is quite a common and famous dataset.