From the course: Building Recommender Systems with Machine Learning and AI

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Accuracy metrics (RMSE and MAE)

Accuracy metrics (RMSE and MAE) - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

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Accuracy metrics (RMSE and MAE)

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- [Instructor] The most straightforward metric is mean absolute error or MAE. Here's the fancy mathematical equation for how to compute it. It's not as complicated as it looks, so let's break it down. Let's say we have n ratings in our test set that we want to evaluate. For each rating, we can call the rating our system predicts, y, and the rating the user actually gave, x. Just take the absolute value of the difference between the two to measure the error for that rating prediction. It's literally just the difference between the predicted rating and the actual rating. We sum those errors up across all n ratings in our test set and divide by n to get the average or mean. So mean absolute error is exactly that, the mean or average absolute values of each error in rating predictions. Remember error is bad, so you want the lowest MAE score you can get, not the highest. Let's look at a simple example. Let's say we just have four ratings in our test set. On the first one, our recommender…

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