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from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
#from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
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y_pred = [1, 0, 0, 1, 0, 0, 1, 0]
y_test = [1, 0, 1, 1, 1, 0, 0, 1]
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cm = confusion_matrix(y_test, y_pred)
cmd = ConfusionMatrixDisplay(cm)
cmd.plot()
plt.show()
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#1. presicion for class 1: 2/3
#2. Recall for class 0: 2/3
#3. Class 1 Recall: 2/5
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print(classification_report(y_test, y_pred)) # first variable is always the True value
#suppport is the sample size
#macro avg is the average of the precision and recall
#weighted avg is the average of the precision and recall weighted by the support

              precision    recall  f1-score   support

           0       0.40      0.67      0.50         3
           1       0.67      0.40      0.50         5

    accuracy                           0.50         8
   macro avg       0.53      0.53      0.50         8
weighted avg       0.57      0.50      0.50         8
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accuracy = accuracy_score(y_test, y_pred) #how to check accuracy by itself
print(accuracy)

0.5
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