Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
array([[ 7, 2],
[ 1, 10]])
All 20 points in our validation set are accounted for. There are three misclassified
points: one false negative and two false positives, just like in the figure above. I
chose to move the blue points (positive) below the line to match Scikit-Learn’s
convention for the confusion matrix.
Confusion matrices are already confusing enough on their own,
but what’s even worse is that you’ll find all sorts of layouts
around. Some people list positives first and negatives last. Some
people even flip actuals and predicted classes, effectively
transposing the confusion matrix. Make sure to always check the
layout before drawing conclusions from matrices you see "in the
wild."
To make your life, and mine, simpler, I am just sticking with Scikit-
Learn’s convention throughout this book.
There is one more thing I hope you noticed already: The confusion matrix depends
on the threshold. If you shift the threshold along the probability line, you’ll end up
changing the number of points in each quadrant.
There are many confusion matrices, one for each threshold.
Moreover, different confusion matrices mean different metrics. We need the
individual components of the confusion matrix, namely, TN, FP, FN, and TP, to
construct those metrics. The function below splits the confusion matrix
accordingly:
244 | Chapter 3: A Simple Classification Problem