Random Forest

In supervised learning algorithms of machine learning, our goal is to learn a stable model that performs well in all aspects. However, the actual situation is often less than ideal, and sometimes we can only obtain multiple biased models (weak supervised models, i.e., models that perform well in certain aspects). Ensemble learning combines these multiple weak supervised models to obtain a better and more comprehensive strong supervised model. The underlying idea of ensemble learning is that even if one weak classifier makes a wrong prediction, other weak classifiers can correct the error. A single learner in an ensemble is typically called a base learner, while the combined ensemble is a strong learner. Ensemble learning itself is not a standalone machine learning algorithm but completes learning tasks by constructing and combining multiple machine learners. Ensemble learning can be used for classification problem integration, regression problem integration, feature selection integration, outlier detection integration, etc., and can be seen in almost all fields of machine learning.