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4 Metrics to Evaluate your Regression Models
Learn the most common metrics you can use to evaluate your regression models — in this article we will explore 4 metrics including their implications and assumptions
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8 min readNov 15, 2021
Regression problems are one of the most common problems to solve with Data Science and Machine Learning. When you want to predict a target with a (theoretical) number of infinite values, you are dealing with a regression problem — some examples are:
- Predicting the income of some person based on their education level, years of experience, etc.;
- Predicting the value of a house based on its characteristics;
- Predicting the return of a stock portfolio based on its composition;
After you develop a regression model, there are a lot of metrics you can choose to evaluate your model - each with its own set of characteristics.
If each metric is different, we can say that they are different representation of the model errors and they can be (mainly) used for two purposes:
- To be used as a final definition for a project success — which metric is the one that communicated to the stakeholders.
- As a Cost Function (the function you want…
