A30 - A30 index

\[\begin{split}\text{A30}(y, \hat{y}) = \frac{1}{n}\sum_{i=1}^{n} \left\{\begin{array}{ll} 1, & \textrm{if } \frac{|\hat{y}_i - y_i|}{y_i} \leq 0.3\\ 0, & \textrm{otherwise} \end{array}\right.\end{split}\]

Latex equation code:

\text{A30}(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 0.3\\ 0, & \textrm{otherwise} \end{array}\right.

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

• a30-index (A30) [12] evaluated metric by showing the number of samples that fit the prediction values with a deviation of ±30%

compared to experimental values. + In other words, the A30 metric measures the proportion of cases where the absolute difference between the predicted and actual values is less than or equal to 30% of the actual value. A higher A30 score indicates better predictive accuracy, as the model is able to make more accurate predictions that are closer to the actual values.

Example to use A30 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)
print(evaluator.a30_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)
print(evaluator.A30(multi_output="raw_values"))