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())