A-Z Machine Learning: Random Forest in Time Series Analysis

A Deep Understanding and Visualization of Random Forest

Random Forest is a robust and versatile machine learning technique often used for prediction and classification tasks. Unlike linear regression, which works with a straight line to make predictions, Random Forest is more like a sophisticated ensemble of decision trees.

This article shows you everything you need to know about random forest, and presents a full example of applying it on time series in order to predict the future values.

Intuition of a Decision Tree

Decision trees are a visual representation of a decision-making process, starting with a root node that represents the initial decision. At each node in the tree, a decision or test is made based on a specific feature, and branches represent possible outcomes. The leaves of the tree signify final outcomes or predicted values.

The decision-making process involves splitting nodes based on particular features, like the number of bedrooms in a house price prediction. Various criteria, such as Gini impurity for…