Accuracy, Recall, Precision and F1 score with sklearn.

accuracy_recall_precision_f1.py

# To be reminded
# 1) Classifying a single point can result in a true positive (truth = 1, guess = 1), a true negative (truth = 0, guess = 0), a false positive (truth = 0, guess = 1), or a false negative (truth = 1, guess = 0).
# 2) Accuracy measures how many classifications your algorithm got correct out of every classification it made.
# 3) Recall measures the percentage of the relevant items your classifier was able to successfully find.
# 4) Precision measures the percentage of items your classifier found that were actually relevant.
# 5) Precision and recall are tied to each other. As one goes up, the other will go down.
# 6) F1 score is a combination of precision and recall.
# 7) F1 score will be low if either precision or recall is low.

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score

labels = [1, 0, 0, 1, 1, 1, 0, 1, 1, 1]
guesses = [0, 1, 1, 1, 1, 0, 1, 0, 1, 0]

print(accuracy_score(labels, guesses))
print(recall_score(labels, guesses))
print(precision_score(labels, guesses))
print(f1_score(labels, guesses))