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ee.ConfusionMatrix.consumersAccuracy

  • ConfusionMatrix.consumersAccuracy() computes the consumer's accuracy for each row of a confusion matrix, representing reliability as correct / total.

  • This method is also known as user's accuracy or specificity and is the complement of commission error.

  • The method returns an Array.

  • The example code demonstrates how to useconsumersAccuracy() along with other accuracy metrics like overall accuracy, producer's accuracy, and kappa statistic.

 Computes the consumer's accuracy (reliability) of a confusion matrix defined as (correct / total) for each row.

UsageReturns
ConfusionMatrix.consumersAccuracy()Array
ArgumentTypeDetails
this:confusionMatrixConfusionMatrix

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.vararray=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]]);varconfusionMatrix=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 usinggeemap for interactive development.

importeeimportgeemap.coreasgeemap

Colab (Python)

# 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)display("Constructed confusion matrix:",confusion_matrix)# Calculate overall accuracy.display("Overall accuracy:",confusion_matrix.accuracy())# Calculate consumer's accuracy, also known as user's accuracy or# specificity and the complement of commission error (1 − commission error).display("Consumer's accuracy:",confusion_matrix.consumersAccuracy())# Calculate producer's accuracy, also known as sensitivity and the# compliment of omission error (1 − omission error).display("Producer's accuracy:",confusion_matrix.producersAccuracy())# Calculate kappa statistic.display("Kappa statistic:",confusion_matrix.kappa())

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Last updated 2023-10-06 UTC.