ee.ConfusionMatrix.kappa

Computes the Kappa statistic for the confusion matrix.

Usage	Returns
`ConfusionMatrix.kappa()`	Float

Argument	Type	Details
this: `confusionMatrix`	ConfusionMatrix

Examples

Code Editor (JavaScript)

// Construct a confusion matrix from an array (rows are actual values,
// columns are predicted values). We construct a confusion matrix here for
// brevity and clear visualization, in most applications the confusion matrix
// will be generated from ee.Classifier.confusionMatrix.
var array = ee.Array([[32, 0, 0,  0,  1, 0],
                      [ 0, 5, 0,  0,  1, 0],
                      [ 0, 0, 1,  3,  0, 0],
                      [ 0, 1, 4, 26,  8, 0],
                      [ 0, 0, 0,  7, 15, 0],
                      [ 0, 0, 0,  1,  0, 5]]);
var confusionMatrix = ee.ConfusionMatrix(array);
print("Constructed confusion matrix", confusionMatrix);

// Calculate overall accuracy.
print("Overall accuracy", confusionMatrix.accuracy());

// Calculate consumer's accuracy, also known as user's accuracy or
// specificity and the complement of commission error (1 − commission error).
print("Consumer's accuracy", confusionMatrix.consumersAccuracy());

// Calculate producer's accuracy, also known as sensitivity and the
// compliment of omission error (1 − omission error).
print("Producer's accuracy", confusionMatrix.producersAccuracy());

// Calculate kappa statistic.
print('Kappa statistic', confusionMatrix.kappa());

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

# Construct a confusion matrix from an array (rows are actual values,
# columns are predicted values). We construct a confusion matrix here for
# brevity and clear visualization, in most applications the confusion matrix
# will be generated from ee.Classifier.confusionMatrix.
array = ee.Array([[32, 0, 0,  0,  1, 0],
                  [ 0, 5, 0,  0,  1, 0],
                  [ 0, 0, 1,  3,  0, 0],
                  [ 0, 1, 4, 26,  8, 0],
                  [ 0, 0, 0,  7, 15, 0],
                  [ 0, 0, 0,  1,  0, 5]])
confusion_matrix = ee.ConfusionMatrix(array)
print("Constructed confusion matrix:")
pprint(confusion_matrix.getInfo())

# Calculate overall accuracy.
print("Overall accuracy:", confusion_matrix.accuracy().getInfo())

# Calculate consumer's accuracy, also known as user's accuracy or
# specificity and the complement of commission error (1 − commission error).
print("Consumer's accuracy:")
pprint(confusion_matrix.consumersAccuracy().getInfo())

# Calculate producer's accuracy, also known as sensitivity and the
# compliment of omission error (1 − omission error).
print("Producer's accuracy:")
pprint(confusion_matrix.producersAccuracy().getInfo())

# Calculate kappa statistic.
print("Kappa statistic:", confusion_matrix.kappa().getInfo())