They average out biases, reduce variance, and are less likely to overfit.
There’s a common line in machine learning which is: “ensemble and get 2%.”
This implies that you can build your models as usual and typically expect a small performance boost from ensembling.
Ensemble methods are often superior to individual models due to several reasons:
- Reduction of Bias and Variance: Ensemble methods combine multiple models, each of which may have different biases and variances. By averaging or combining their predictions, ensemble methods can reduce both bias and variance, leading to better overall performance.
- Improved Generalization: Ensemble methods can improve generalization by leveraging the diversity of models within the ensemble. Each model may capture different aspects of the data, leading to a more comprehensive understanding and better predictive performance on unseen data.
- Robustness to Noise: Ensemble methods are often more robust to noise and outliers in the data. Since individual models may make errors on certain instances, combining their predictions can help mitigate the impact of these errors and provide more robust predictions.
- Handling Complex Relationships: Ensemble methods can capture complex relationships in the data by combining simpler models. For example, in a Random Forest, each decision tree in the ensemble may capture different subsets of features or interactions, leading to a more nuanced understanding of the data.
- Reduced Overfitting: Ensemble methods can help reduce overfitting, especially in complex models or when dealing with high-dimensional data. By combining multiple models, ensemble methods can prevent any single model from memorizing the training data and instead encourage generalization.
- Flexibility and Adaptability: Ensemble methods are flexible and can be adapted to various types of data and modeling tasks. Different ensemble techniques, such as bagging, boosting, or stacking, offer different trade-offs and can be chosen based on the specific characteristics of the problem at hand.
Overall, ensemble methods leverage the wisdom of crowds, combining the strengths of multiple models to achieve better predictive performance than any individual model could achieve alone.