Assume that y is the actual value in array form, and there is a trained model, with dependent variables X in array form.
Confusion matrix #
from sklearn.metrics import confusion_matrix
confusion_matrix(y, model.predict(X))
If binary, this returns a 2x2 array, where top left is true negative, bottom left is false negative, top right is false positive, bottom right is true positive.
Generate a more detailed report with:
from sklearn.metrics import classification_report
print(classification_report(y, model.predict(X)))
Visualize with this snippet:
cm = confusion_matrix(y, model.predict(x))
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(cm)
ax.grid(False)
ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))
ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))
ax.set_ylim(1.5, -0.5)
for i in range(2):
for j in range(2):
ax.text(j, i, cm[i, j], ha='center', va='center', color='red')
plt.show()