CI - Confidence Index

\[\text{CI}(y, \hat{y}) = \text{R}(y, \hat{y}) * \text{WI}(y, \hat{y})\]

Latex equation code:

\text{CI}(y, \hat{y}) = \text{R}(y, \hat{y}) * \text{WI}(y, \hat{y})

Confidence Index [10] or Performance Index (CI/PI) is score that measures the performance of each estimation method, with a higher value indicating better performance. The range of the CI/PI is (-inf, 1], meaning it can take any value less than or equal to 1, but not including negative infinity.

• Best possible score is 1.0, bigger value is better. Range = (-inf, 1], meaning of values:

  > 0.85          Excellent Model
  0.76-0.85       Very good
  0.66-0.75       Good
  0.61-0.65       Satisfactory
  0.51-0.60       Poor
  0.41-0.50       Bad
  < 0.40          Very bad
  

Example to use CI 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.confidence_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.CI(multi_output="raw_values"))