What is ensemble learning?
Ensemble learning solves a single prediction problem by building several models. It works by generating multiple classifiers/models that learn and make predictions independently. These predictions are finally combined into combined predictions, which are therefore better than any one single-class prediction.
Two core tasks of machine learning
Task 1: How to optimize training data —> mainly used to solve underfitting problems
Task 2: How to improve generalization performance —> mainly used to solve the problem of overfitting
Boosting and Bagging in integrated learning
As long as the performance of a single classifier is not too bad, the result of ensemble learning is always better than that of a single classifier