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
.
This exercise is part of the course
Model Validation in Python
Exercise instructions
- 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.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.metrics import mean_absolute_error
# Manually calculate the MAE
n = ____(predictions)
mae_one = sum(____(y_test - predictions)) / n
print('With a manual calculation, the error is {}'.format(____))
# Use scikit-learn to calculate the MAE
mae_two = ____(____, ____)
print('Using scikit-learn, the error is {}'.format(____))