In Chapter 2, Diabetes Prediction with Multilayer Perceptrons, we saw how the confusion matrix is a useful visualization tool to evaluate the performance of our model. Let's also use the confusion matrix to evaluate the performance of our model in this project.
To recap, these are the definitions of the terms in the confusion matrix:
- True negative: The actual class is negative (negative sentiment), and the model also predicted negative
- False positive: The actual class is negative (negative sentiment), but the model predicted positive
- False negative: The actual class is positive (positive sentiment), but the model predicted negative
- True positive: The actual class is positive (positive sentiment), and the model predicted ...
