Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks.
Author: Ruslan Shudra
Let's summarize and highlight the main information covered in the course.
Ensemble learning
in machine learning is a technique that combines the predictions of
multiple individual models (learners) to produce a more robust and
accurate prediction or classification. It leverages the principle that
by aggregating the opinions of multiple models, you can often achieve
better results than relying on a single model.
There are three commonly used techniques for creating ensembles: bagging, boosting, and stacking.
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