What is ensemble learning?

Ensemble learning is a powerful approach in machine learning that enhances the predictive performance by combining the outputs of multiple models. The key idea is that by aggregating predictions from several diverse models, you can achieve a more robust and accurate result than any single model could deliver on its own. This method leverages the strengths of different algorithms, which can capture various patterns in the data, leading to improved accuracy and generalization capabilities.

For example, popular ensemble methods include bagging, boosting, and stacking, each of which has its own way of combining model predictions. Bagging reduces variance by training multiple models independently and combining their results, while boosting focuses on training models sequentially by emphasizing formerly misclassified instances. The combination of these strategies results in a more reliable final output, which is particularly beneficial in complex tasks such as classification and regression.

The other options provide different techniques or frameworks that do not align with the concept of ensemble learning. For instance, selecting a single best model is contrary to the essence of ensemble learning, which is all about leveraging multiple models rather than narrowing down to one. Similarly, standardizing AI model evaluation or focusing on unsupervised learning methods does not encapsulate the essence of combining predictions from multiple sources to enhance performance.