ee.ConfusionMatrix.producersAccuracy

Tính độ chính xác của nhà sản xuất trong ma trận nhầm lẫn được xác định là (số lượng đúng / tổng số) cho từng cột.

Cách sử dụng	Giá trị trả về
ConfusionMatrix.producersAccuracy()	Mảng

Đối số	Loại	Thông tin chi tiết
this: confusionMatrix	ConfusionMatrix

Ví dụ

Trình soạn thảo mã (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());

Thiết lập Python

Hãy xem trang Môi trường Python để biết thông tin về API Python và cách sử dụng geemap cho quá trình phát triển tương tác.

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())