Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.
The correct answer to why ensemble learning is used in machine learning can be summarized in several key points:
- Improved Accuracy: Ensemble methods combine multiple models to produce better predictive performance than any individual model. By aggregating predictions from multiple models, ensemble methods can reduce the variance and bias, leading to improved accuracy.
- Robustness: Ensemble methods are less susceptible to overfitting compared to individual models. If one model overfits to the training data, the impact can be mitigated by combining it with other models that might not be affected in the same way.
- Stability: Ensemble methods tend to be more stable and reliable across different datasets. They can generalize better to unseen data because they leverage diverse sets of models trained on different subsets or representations of the data.
- Capturing Complex Relationships: Different models may capture different aspects or representations of the underlying data. Ensemble methods allow for the combination of these diverse perspectives, potentially capturing complex relationships that individual models might miss.
- Versatility: Ensemble methods can be applied to various types of machine learning tasks, including classification, regression, and clustering. They are compatible with a wide range of base learners, including decision trees, neural networks, support vector machines, etc.
- Scalability: Ensemble methods can scale well to large datasets and complex problems. They can handle high-dimensional data efficiently and are adaptable to distributed computing environments.
- State-of-the-Art Performance: Ensemble methods have been shown to achieve state-of-the-art performance in many machine learning competitions and real-world applications. They are widely used in practice across various domains due to their effectiveness.
Overall, ensemble learning is a powerful technique that leverages the wisdom of crowds, combining multiple models to achieve superior predictive performance, robustness, and generalization capabilities compared to individual models.