From the course: Artificial Intelligence Foundations: Machine Learning
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Exploring common regression metrics
From the course: Artificial Intelligence Foundations: Machine Learning
Exploring common regression metrics
- [Instructor] Metrics are key indicators of whether or not your model is well performing, or whether you'll need to tweak the hyperparameters to continue your training iterations. Today, we'll cover several metrics reserved for regression problems. R squared, mean squared error, root mean square error, and mean absolute error. Let's talk about them now. We've used R squared throughout the course to evaluate our home cost predicting model. If you recall, R squared calculates the difference between the actual values and the predictions made by the model. The distances between the actual and predicted values are called residuals. Residuals are key to determining the performance of a regression model. R squared values land between zero to one. Values closer to one indicate a model that has a better fit between predicted and actual values. R squared is more of a relative measure, while mean square error and root mean square…
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