here' the final resulttest_mae_score you're still off by about $ , wrapping up here' what you should take away from this exampleregression is done using different loss functions than what we used for classification mean squared error (mseis loss function commonly used for regression similarlyevaluation metrics to be used for regression differ from those used for classificationnaturallythe concept of accuracy doesn' apply for regression common regression metric is mean absolute error (maewhen features in the input data have values in different rangeseach feature should be scaled independently as preprocessing step when there is little data availableusing -fold validation is great way to reliably evaluate model when little training data is availableit' preferable to use small network with few hidden layers (typically only one or two)in order to avoid severe overfitting licensed to