![]() Put simply, it measures the extent to which the model features can be used to explain the model target.įor example, an R Squared value of 0.9 would imply that 90% of the target variance can be explained by the model features, whilst a value of 0.2 would suggest that the model features are only able to account for 20% of the variance. R Squared can be interpreted as the percentage of the dependent variable variance which is explained by the independent variables. The formula for calculating R Squared is as follows: How to interpret R Squared R Squared measures how much of the dependent variable variation is explained by the independent variables in the model. Unlike other metrics, such as MAE or RMSE, it is not a measure of how accurate the predictions are, but instead a measure of fit. ![]() R Squared (also known as R2) is a metric for assessing the performance of regression machine learning models. In this post, I explain what R Squared is, how to interpret the values and walk through an example. R Squared is a common regression machine learning metric, but it can be confusing to know how to interpret the values.
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