Evaluate the model on a test set
After fitting the model, you can evaluate it on new data. You will give the model a new X
matrix (also called test data), allow it to make predictions, and then compare to the known y
variable (also called target data).
In this case, you'll use data from the post-season tournament to evaluate your model. The tournament games happen after the regular season games you used to train our model, and are therefore a good evaluation of how well your model performs out-of-sample.
The games_tourney_test
DataFrame along with the fitted model
object is available in your workspace.
This exercise is part of the course
Advanced Deep Learning with Keras
Exercise instructions
- Assign the test data (
seed_diff
column) toX_test
. - Assign the target data (
score_diff
column) toy_test
. - Evaluate the model on
X_test
andy_test
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load the X variable from the test data
X_test = ____
# Load the y variable from the test data
y_test = ____
# Evaluate the model on the test data
print(model.____(____, ____, verbose=False))