True Positive, True Negative, False Positive, False Negative in scikit learn 1

True Positive, True Negative, False Positive, False Negative in scikit learn

#According to scikit-learn documentation,

#http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix

#By definition a confusion matrix C is such that C[i, j] is equal to the number of observations known to be in group i but predicted to be in group j.

#Thus in binary classification, the count of true negatives is C[0,0], false negatives is C[1,0], true positives is C[1,1] and false positives is C[0,1].

CM = confusion_matrix(y_true, y_pred)

TN = CM[0][0]
FN = CM[1][0]
TP = CM[1][1]
FP = CM[0][1]

Here is what the above code is Doing:
1. We are creating a confusion matrix CM using the confusion_matrix function from sklearn.metrics.
2. We are then creating four variables TN, FN, TP, FP which are the four cells of the confusion matrix.

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