Ensemble Learning is the technique of combining multiple models to produce a better result than any single model could achieve alone. The combined “ensemble” is more accurate and robust.
What it means in plain English
The core idea is that many models, each with its own strengths and weaknesses, can be combined so their errors cancel out and their strengths add up — much like consulting a panel of experts rather than one. Ensembles combine predictions by voting or averaging. It is a reliable way to squeeze better performance out of machine learning.
Random forests and gradient boosting are two of the most successful ensemble methods.
A simple example
Instead of relying on one model to predict a stock’s movement, an ensemble runs several different models and combines their predictions — smoothing out the biases of any single one for a more reliable forecast.
Why it matters
Ensemble learning is behind many of the most accurate machine learning systems, including the tree-based methods that dominate structured-data tasks. The principle that combined models beat individuals is one of the most useful ideas in the field.
Related terms
- Random Forest — an ensemble of decision trees.
- Gradient Boosting — a sequential ensemble method.
- Model — the components an ensemble combines.
Frequently asked questions
What is ensemble learning?
It combines the predictions of multiple models to produce a result that is usually more accurate and robust than any single model alone.
What are common ensemble methods?
Bagging (like random forests), boosting (like gradient boosting), and stacking are widely used ensemble techniques.
