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Accuracy, Precision, Recall, Specificity, F1

 

Before calculating the various evaluation indicators, the confusion matrix needs to be calculated first:

True Positive , TP： positive samples predicted as positive by the model.

False Positive , FP： negative samples predicted as positive by the model.

False Negative , FN： positive samples predicted as negative by the model.

True Negative , TN： negative samples predicted as negative by the model.

The calculation formula of each index is as follows:

Accuracy, ACC=(TP+TN)/N,

Precision, PPV=TP/(TP+FP),

Sensitivity/Recall, TPR=TP/(TP+FN),

Specificity, TNR=TN/(FP+TN),

F1=2*Precision*Recall/(Precision+Recall)

For example, in the titanic_export.csv data, there are the true value of the target “Survived“ and the predicted value “Survived_predict“ with a threshold of 0.5, and the probability predicted value “Survived_1_percentage“ of the target variable being 1

Survived_1_percentage

PassengerId

Survived

Survived_predict

0.146539017

1

0

0

0.966226989

2

1

1

0.644612591

3

1

1

0.970606942

4

1

1

0.096753954

5

0

0

…

…

…

…

Evaluate the performance of the the predict value “Survived_predict“ with a threshold of 0.5

A

B

1

=T("D://titanic_export.csv")

2

=A1.select(Survived==1 && Survived_predict==1)

3

=A1.select(Survived==0 && Survived_predict==1)

4

=A1.select(Survived==1 && Survived_predict==0)

5

=A1.select(Survived==0 && Survived_predict==0)

6

>TP=A2.len(),FP=A3.len(),FN=A4.len(),TN=A5.len(),N=A1.len()

7

=(TP+TN)/N

/Accuracy

8

=TP/(TP+FP)

/Precision

9

=TP/(TP+FN)

/Sensitivity/Recall

10

=TN/(FP+TN)

/Specificity

11

=2*A8*A9/(A8+A9)

/F1

A2 The positive samples predicted by the model to be positive

A3 The negative samples predicted by the model to be positive

A4 The positive samples predicted by the model to be negative

A5 The negative samples predicted by the model to be negative

A6 Calculate TP，FP，FN，TN，N

A7 Calculate the Accuracy

..

A8-A11 Similarly, calculate other indicators according to the formula


 
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