How to Reduce Variance in a Final Machine Learning Model

A final machine learning model is one trained on all available data and is then used to make predictions on new data. A problem with most final models is that they suffer variance in their predictions. This means that each time you fit a model, you get a slightly different set of parameters that in turn will make slightly different predictions. Sometimes more and sometimes less skillful than what you expected. This can be frustrating, especially when you are looking to deploy a model into an operational environment.