ee.ConfusionMatrix.accuracy

सही / कुल के तौर पर तय की गई कंफ्यूज़न मैट्रिक्स की कुल सटीकता का हिसाब लगाता है.

इस्तेमाल	रिटर्न
ConfusionMatrix.accuracy()	फ़्लोट

आर्ग्यूमेंट	टाइप	विवरण
यह: confusionMatrix	ConfusionMatrix

उदाहरण

कोड एडिटर (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 सेटअप करना

Python API और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के बारे में जानकारी पाने के लिए, Python एनवायरमेंट पेज देखें.

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())