![]() ![]() Models will look at other aspects of the data called inputs that we believe affect the outputs and use them to generate estimated outputs. In general, these models deal with the prediction and estimation of values of interest in our data called outputs. In the context of regression, models refer to mathematical equations used to describe the relationship between two variables. The intuition behind many of the metrics we’ll cover here extends to other types of models and their respective metrics. Linear regression is the most commonly used model in research and business and is the simplest to understand, so it makes sense to start developing your intuition on how they are assessed. There are many types of regression, but this article will focus exclusively on metrics related to linear regression. This article will dive into four common regression metrics and discuss their use cases. These metrics are short and useful summaries of the quality of our data. The quality of a regression model is how well its predictions match up against actual values, but how do we actually evaluate quality? Luckily, smart statisticians have developed error metrics to judge the quality of a model and enable us to compare regressions against other regressions with different parameters. Regressions are one of the most commonly used tools in a data scientist’s kit. ![]()
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