Random Forest

Random forest

Random forest is a popular machine learning algorithm that belongs to the category of ensemble learning methods, particularly bagging. It combines the predictions of multiple individual decision trees to make more accurate and robust predictions.

Random forest is a versatile algorithm that utilizes the power of decision trees. It constructs many decision trees and combines their outputs to obtain final predictions. Each decision tree is built using a random subset of the training data and a random subset of features. By averaging the predictions of these individual trees, Random forest can reduce overfitting and improve generalization.

Random sampling

Random forest employs random sampling with replacement, also known as bootstrapping, to create different subsets of the training data for each decision tree. In a random forest, each subset has the same number of data points as in the original dataset. In this process, a random sample is drawn from the original dataset, allowing the data point to be selected multiple times while others may be excludedRandomForest . This random sampling approach introduces diversity among the decision trees, enabling them to capture different aspects of the data.

Random feature selection

In addition to random sampling, random forest performs random feature selection for each individual decision tree. Instead of considering all the available features, a random subset of features is selected for building each tree. Random forest ensures that no single feature dominates the decision-making process. Including different subsets of features in each tree improves the overall performance and generalization capability.

In some datasets, certain features may be more strongly correlated with the target variable, making them appear more important. However, this can lead to overlooking other features that could also be valuable for making accurate predictions. Random forest tackles this issue by creating multiple decision trees, each trained on a different subset of the data.

Note: By introducing randomness in the selection of data points and features for each tree, the algorithm ensures that no single feature dominates the predictions.

Why do we use random forest?

There are several reasons why random forest is a popular choice for many machine learning tasks:

• Robustness: Random forests are less prone to overfitting than individual models (single decision trees). The ensemble approach helps to reduce bias and variance, leading to more reliable predictions.

• Feature importance: Random forest provides a measure of feature importance, enabling us to understand the relevance of different features in the prediction process. This information can be valuable for feature selection and data exploration.

• Handling large datasets: Random forest can handle large datasets with a high number of features efficiently. It can effectively deal with missing values and maintain good performance even with imbalanced class distributions.

Implementation from scratch

It involves building multiple decision trees, performing random sampling, and aggregating the ...