Confusion matrices, again
Creating a confusion matrix in Python is simple. The biggest challenge will be making sure you understand the orientation of the matrix. This exercise makes sure you understand the sklearn
implementation of confusion matrices. Here, you have created a random forest model using the tic_tac_toe
dataset rfc
to predict outcomes of 0 (loss) or 1 (a win) for Player One.
Note: If you read about confusion matrices on another website or for another programming language, the values might be reversed.
This exercise is part of the course
Model Validation in Python
Exercise instructions
- Import
sklearn
's function for creating confusion matrices. - Using the model
rfc
, create category predictions on the test setX_test
. - Create a confusion matrix using
sklearn
. - Print the value from
cm
that represents the actual 1s that were predicted as 1s (true positives).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.metrics import ____
# Create predictions
test_predictions = rfc.____(____)
# Create and print the confusion matrix
cm = ____(____, ____)
print(cm)
# Print the true positives (actual 1s that were predicted 1s)
print("The number of true positives is: {}".format(cm[____, ____]))