Learn about Regressions Open_Closed, including how to measure it, and leverage it in dashboards and visualizations with Metabase.
Regressions open/closed is a method of machine learning model evaluation you can visualize to help understand the performance of your regression model in-depth. There are plenty of metrics you can use to determine the reliability of a model, but regression models require specific metrics to have accurate results. It’s a little more difficult to figure out the accuracy of a regression model as the nature of a regression model differs from how other types of model classification works. It’s always a good idea to figure out the accuracy and integrity of your machine learning, so the extra steps it takes to do so for a regression model is no exception. Looking at how your regression model is fairing in terms of accuracy can help guide improvements, just remember that machine learning is rarely 100% accurate, and don’t fall into the trap of overworking a project. You’ll end up wasting time and energy that way.
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There are a few different regression model calculations that can help determine accuracy. R square/adjusted R square - Explains how much variability from the dependent variable can be explained by the regression model. Mean square error(MSE) - Useful to determine how well a regression model fits dependent variables. Mean absolute error(MAE) - Another calculation similar to MSE, giving a bigger penalization to big prediction errors instead of treating all errors equally. All of these together can help you learn more about how well your regression models are working.
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