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Can anyone tell me more about this Goodness of fitPrediction quality measure, Prediction Accuracy, used for regression evaluation?

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I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

Also, the highest possible value with PA is 1 but the output of the formula can givegives any value as can be seen in this code, or is the code wrong?

y_value = [2,4,6,5,8,10]
y_value_test = [1,3,6,7,9,11]

y_value_bar = np.mean(y_value)
y_value_test_bar = np.mean(y_value_test) 

upper = np.sum(y_value - y_value_bar)
lower = np.sum(y_value_test - y_value_test_bar)

result = upper/lower
result

enter image description here

I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

Also, the highest value is 1 but the output of the formula can give any value as can be seen in this code, or is the code wrong?

y_value = [2,4,6,5,8,10]
y_value_test = [1,3,6,7,9,11]

y_value_bar = np.mean(y_value)
y_value_test_bar = np.mean(y_value_test) 

upper = np.sum(y_value - y_value_bar)
lower = np.sum(y_value_test - y_value_test_bar)

result = upper/lower
result

enter image description here

I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

Also, the highest possible value with PA is 1 but the output of the formula gives any value as can be seen in this code, or is the code wrong?

y_value = [2,4,6,5,8,10]
y_value_test = [1,3,6,7,9,11]

y_value_bar = np.mean(y_value)
y_value_test_bar = np.mean(y_value_test) 

upper = np.sum(y_value - y_value_bar)
lower = np.sum(y_value_test - y_value_test_bar)

result = upper/lower
result

enter image description here

added 434 characters in body
Source Link

I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

Also, the highest value is 1 but the output of the formula can give any value as can be seen in this code, or is the code wrong?

y_value = [2,4,6,5,8,10]
y_value_test = [1,3,6,7,9,11]

y_value_bar = np.mean(y_value)
y_value_test_bar = np.mean(y_value_test) 

upper = np.sum(y_value - y_value_bar)
lower = np.sum(y_value_test - y_value_test_bar)

result = upper/lower
result

enter image description here

I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

enter image description here

I am trying to find more info about the attached Prediction Accuracy measure used for regression. It is quiet similar to R2 and Nash-sutcliffe Efficiency but not exactly. Googling leads to classification accuracy measures, which is wrong.

Any links, sources or info about the measure is appreciated. Also, is there an API to implement it directly in Python?

Also, the highest value is 1 but the output of the formula can give any value as can be seen in this code, or is the code wrong?

y_value = [2,4,6,5,8,10]
y_value_test = [1,3,6,7,9,11]

y_value_bar = np.mean(y_value)
y_value_test_bar = np.mean(y_value_test) 

upper = np.sum(y_value - y_value_bar)
lower = np.sum(y_value_test - y_value_test_bar)

result = upper/lower
result

enter image description here

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