Ensemble Machine Learning

Ensemble Machine Learning

Ensemble machine learning is a powerful approach that combines multiple weak learning models, also known as base models, to improve the accuracy and stability of predictions. This approach is particularly useful in complex problems where a single model may struggle to capture all the nuances and variations of the data. The combination of weak models can produce more accurate and stable models when they are properly combined. It is important to note that an appropriate ensemble model must have a low bias and variance, both of which are essential and desirable, but often have an inverse relationship. Basic models rarely perform well on their own due to their high bias or variance.

Obtaining good results from ensemble machine learning models requires the right choice of algorithms and the right combination of base models. In many cases, especially bagging and boosting methods, a single basic learning algorithm is used, so we have a number of the same basic models that are trained in different ways. The choice of model depends on several variables in the problem, including the amount of data, the dimensions of the data, etc.

There are three general methods for combining base models in ensemble machine learning:

• Bagging Method
• Boosting Method
• Stacking Method