CE - Cross Entropy
\[\text{CE}(y, \hat{y}) = -\frac{1}{n}\sum_{i=1}^{n} \left[y_i\log(\hat{y}_i) + (1-y_i)\log(1-\hat{y}_i)\right]\]
Latex equation code:
\text{CE}(y, \hat{y}) = -\frac{1}{n}\sum_{i=1}^{n} \left[y_i\log(\hat{y}_i) + (1-y_i)\log(1-\hat{y}_i)\right]
Range = (-inf, 0]. Can’t give comment about this one
Greater value of Entropy, the greater the uncertainty for probability distribution and smaller the value the less the uncertainty
Example to use CE 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.cross_entropy())
## 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.CE(multi_output="raw_values"))