One method of judging the quality of a particular model is by residuals. That means the model is fit using all the data points and the prediction for each data point is compared with its actual output. The absolute value of each error is taken and the mean of those values is computed to arrive at the mean absolute residual error. Models with lower values of this measure are deemed to be better.

There are always a plethora of metrics in machine learning that can be used to evaluate the performance of a ML model. This is an attempt to draw a metric map, just to keep them all in one place.

Know some more metrics? Start here

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