Now let’s evaluate the quality of the model by comparing the predictions made on the 20% test data to the actual fare amounts, and calculate the regression evaluation metrics.
So imagine you take a taxi trip in New York city an you use your model to predict the fare beforehand. What kind of prediction error would you consider acceptable?
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