Exercise

# Mean absolute error

Communicating modeling results can be difficult. However, most clients understand that on average, a predictive model was off by some number. This makes explaining the mean absolute error easy. For example, when predicting the number of wins for a basketball team, if you predict 42, and they end up with 40, you can easily explain that the error was two wins.

In this exercise, you are interviewing for a new position and are provided with two arrays. `y_test`

, the true number of wins for all 30 NBA teams in 2017 and `predictions`

, which contains a prediction for each team. To test your understanding, you are asked to both manually calculate the MAE and use `sklearn`

.

Instructions

**100 XP**

- Manually calculate the MAE using
`n`

as the number of observations predicted. - Calculate the MAE using
`sklearn`

. - Print off both accuracy values using the print statements.