The Random Forest is also known as Decision Tree Forest. It is one of the popular decision tree-based ensemble models. The accuracy of these models is higher than other decision trees. This algorithm is used for both classification and regression applications.

Random Forest is a popular ensemble learning algorithm used in machine learning, particularly for classification and regression tasks. It was introduced by Leo Breiman in 2001. The main idea behind Random Forest is to build a multitude of decision trees during the training phase and combine their predictions during the testing phase.

Here’s a step-by-step explanation of how Random Forest works:

Bootstrap Sampling (Bagging): Random Forest starts by creating multiple bootstrap samples from the original dataset. A bootstrap sample is created by randomly sampling with replacement from the original dataset. This means that some instances may be duplicated, while others may be left out.
Random Feature Selection: For each decision tree in the forest, a random subset of features is selected at each node for splitting. This helps in decorrelating the trees and ensures that no single feature dominates the decision-making process.
Decision Tree Construction: A decision tree is built for each bootstrap sample using the randomly selected features. The trees are typically grown deep, allowing them to capture complex patterns in the data.
Voting or Averaging: During the testing phase, predictions from each individual tree are collected. For classification tasks, the final prediction is often determined by a majority vote, where the class with the most votes becomes the predicted class. For regression tasks, the final prediction is often the average of the individual tree predictions.

Random Forest has several advantages:

Reduced Overfitting: By building multiple trees and combining their predictions, Random Forest tends to be more robust and less prone to overfitting compared to individual decision trees.
High Accuracy: Random Forest often provides high accuracy in both classification and regression tasks.
Implicit Feature Selection: The random feature selection process allows Random Forest to implicitly perform feature selection, focusing on the most informative features.
Handles Missing Values: Random Forest can handle missing values without the need for imputation.
Parallelization: Training and prediction in Random Forest can be parallelized, making it efficient for large datasets.

Random Forest is a versatile and powerful algorithm that is widely used in practice for its ability to provide accurate and stable predictions across various types of datasets.