A20 - A20 index

\[\text{A20}(y, \hat{y}) =\]

• Best possible score is 1.0, bigger value is better. Range = [0, 1]

• a20-index evaluated metric by showing the number of samples that fit the prediction values with a deviation of ±20% compared to experimental values

• I developed this metric based on a10-index

Latex equation code:

\text{A20}(y, \hat{y}) =

Example to use A20 metric:

from numpy import array
from permetrics.regression import RegressionMetric

## For 1-D array
y_true = array([3, -0.5, 2, 7])
y_pred = array([2.5, 0.0, 2, 8])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)
print(evaluator.a20_index())

## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6]])
y_pred = array([[0, 2], [-1, 2], [8, -5]])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)
print(evaluator.a20(multi_output="raw_values"))