Evaluate The Results

Lesson 10 of 17

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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