Ensemble Learning
Ensemble learning is a machine learning paradigm where multiple models, called individual learners or base learners, are trained to solve the same problem and then combined to achieve better predictive performance than any single model could. This approach leverages the 'wisdom of crowds' by aggregating the predictions of diverse models. Techniques like Bagging, Boosting, and Stacking are popular ensemble methods, each employing different strategies to combine learners whether through parallel training, sequential training, or meta-learning respectively.
It's like having a committee of experts, each with their own unique perspective, discussing an issue to arrive at a more robust and accurate collective decision than any single expert could.
Ensemble methods often achieve higher accuracy and are more robust to noise and individual model errors, leading to improved generalization and reliability in real-world applications.
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