Knowing the characteristics of machine learning predictive models will help you understand the advantages and limitations in comparison to any statistical or decision tree models.
Let's get some insights on a few characteristics of predictive models in machine learning:
- Optimized to learn complex patterns: Machine learning models are designed to be optimized to learn complex patterns. In comparison to statistical models or decision tree models, predictive models greatly excel, when you have very complex patterns in data.
- Account for interactions and nonlinear relationships: Machine learning predictive models can account for interactions in the data and nonlinear relationships to an even better degree than decision tree models.
- Few assumptions: These models are powerful because they have very few assumptions. They can also be used with different types of data.
- A black box model's interpretation is not straightforward: Predictive models are black box models, this is one of the drawbacks of predictive machine learning models, because this implies that the interpretation is not straightforward. This means that, if we end up building many different equations and combine them, it becomes very difficult to see exactly how each one of these variables ended up interacting and impacting an output variable. So, the predictive machine learning models are great when it comes to predictive accuracy, but they're not that good for understanding the mechanics behind a prediction.
If you want to predict something, these models do a pretty good job and have amazing accuracy. But if you want to know why something is being predicted, and if you are looking forward to making some changes in the implementation so that you don't get a particular prediction, then it would be difficult to decipher.
