Definition: Ensemble learning is a machine learning technique that combines multiple models to improve performance and reduce overfitting.

Detailed Description:

Ensemble methods involve training multiple models on the same dataset and combining their predictions to make final decisions. Common ensemble techniques include:

• Bagging: Creating multiple models from different subsets of the data and averaging their predictions.
• Boosting: Iteratively training models, focusing on examples that were misclassified by previous models.
• Stacking: Combining the predictions of multiple models using a meta-learner.

Ensemble learning can improve the accuracy and robustness of machine learning models, especially when dealing with complex problems.

Related Terms:

• Machine Learning
• Model Combination
• Bagging
• Boosting
• Stacking

FAQs:

1. What are the benefits of using ensemble learning?

   • Ensemble learning can improve model accuracy, reduce overfitting, and increase robustness.

2. How is bagging different from boosting?

   • Bagging creates multiple models from different subsets of the data, while boosting focuses on improving the performance of subsequent models by weighting examples that were misclassified by previous models.

3. When should ensemble learning be used?

   • Ensemble learning is often used for complex problems where individual models may not perform well.