Intermediate · Data
Random forest
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. An ensemble machine learning method that builds multiple decision trees to improve prediction accuracy and control overfitting.
Technical Definition
An ensemble machine learning method that builds multiple decision trees to improve prediction accuracy and control overfitting.
How it works
Random Forest is an ensemble learning technique that constructs a multitude of decision trees during training. For classification tasks, it outputs the most frequent class predicted by individual trees, and for regression, it outputs the average prediction. This approach helps to mitigate the overfitting common in single decision trees, leading to more robust and accurate predictions.
Related Concepts
- Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Ensemble learning — Combining multiple machine learning models to improve overall prediction accuracy and robustness.