A20 - A20 index
\[\begin{split}\text{A20}(y, \hat{y}) = \text{A10}(y, \hat{y}) = \frac{1}{n}\sum_{i=1}^{n} \left\{\begin{array}{ll} 1, & \textrm{if } \frac{|\hat{y}_i - y_i|}{y_i} \leq 0.2\\ 0, & \textrm{otherwise} \end{array}\right.\end{split}\]
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
In other words, the A20 metric measures the proportion of cases where the absolute difference between the predicted and actual values is less than or equal
to 20% of the actual value. A higher A20 score indicates better predictive accuracy, as the model is able to make more accurate predictions that are closer to the actual values.
Latex equation code:
\text{A20}(y, \hat{y}) = \frac{1}{n}\sum_{i=1}^{n} \left\{\begin{array}{ll} 1, & \textrm{if } \frac{|\hat{y}_i - y_i|}{y_i} \leq 0.2\\ 0, & \textrm{otherwise} \end{array}\right.
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"))