One way to improve a machine learning model is to not make just one model. Instead, you can make several models that all have different strengths and weaknesses. This combination of diverse abilities can allow for much more accurate predictions. The use of multiple models is know as ensemble learning. This post will provide insights into ensemble learning as they are used in developing machine models. The Major ChallengeThe biggest challenges in creating an ensemble of models is deciding what models to develop and how the various models are combined to make predictions. To deal with these challenges involves the use of training data and several different functions. Developing an ensemble model begins with training data. The next step is the use of some sort of allocation function. The allocation function determines how much data each model receives in order to make predictions. For example, each model may receive a subset of the data or limit how many features each model can use. However, if several different algorithms are used the allocation function may pass all the data to each model with making any changes. After the data is allocated, it is necessary for the models to be created. From there, the next step is to determine how to combine the models. The decision on how to combine the models is made with a combination function. The combination function can take one of several approaches for determining final predictions. For example, a simple majority vote can be used which means that if 5 models where develop and 3 vote “yes” than the example is classified as a yes. Another option is to weight the models so that some have more influence then others in the final predictions. Benefits of Ensemble LearningEnsemble learning provides several advantages. One, ensemble learning improves the generalizability of your model. With the combine strengths of many different models and or algorithms it is difficult to go wrongTwo, ensemble learning approaches allow for tackling large datasets. The biggest enemy to machine learning is memory. With ensemble approaches, the data can be broken into smaller pieces for each model. Ensemble learning is yet another critical tool in the data scientist’s toolkit. The complexity of the world today makes it too difficult to lean on a singular model to explain things. Therefore, understanding the application of ensemble methods is a necessary step.