공지사항:
2025년 4월 15일 전에 Earth Engine 사용을 위해 등록된 모든 비상업용 프로젝트는 Earth Engine 액세스를 유지하기 위해
비상업용 자격 요건을 인증해야 합니다.
ee.ConfusionMatrix.array
컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
혼동 행렬을 배열로 반환합니다.
| 사용 | 반환 값 |
|---|
ConfusionMatrix.array() | 배열 |
| 인수 | 유형 | 세부정보 |
|---|
다음과 같은 경우: confusionMatrix | ConfusionMatrix | |
예
코드 편집기 (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("ee.ConfusionMatrix", confusionMatrix);
print("ee.ConfusionMatrix as ee.Array", confusionMatrix.array());
Python 설정
Python API 및 geemap를 사용한 대화형 개발에 관한 자세한 내용은
Python 환경 페이지를 참고하세요.
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("ee.ConfusionMatrix:")
pprint(confusion_matrix.getInfo())
print("ee.ConfusionMatrix as ee.Array:")
pprint(confusion_matrix.array().getInfo())
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-07-26(UTC)
[null,null,["최종 업데이트: 2025-07-26(UTC)"],[[["\u003cp\u003eThe \u003ccode\u003eConfusionMatrix.array()\u003c/code\u003e function returns the confusion matrix as an \u003ccode\u003eee.Array\u003c/code\u003e object.\u003c/p\u003e\n"],["\u003cp\u003eThis function is used to access the underlying data of an \u003ccode\u003eee.ConfusionMatrix\u003c/code\u003e object for further analysis or visualization.\u003c/p\u003e\n"],["\u003cp\u003eThe confusion matrix represents the performance of a classifier, with rows indicating actual values and columns indicating predicted values.\u003c/p\u003e\n"],["\u003cp\u003eYou can create an \u003ccode\u003eee.ConfusionMatrix\u003c/code\u003e from an existing \u003ccode\u003eee.Array\u003c/code\u003e or by using the \u003ccode\u003eee.Classifier.confusionMatrix()\u003c/code\u003e function.\u003c/p\u003e\n"]]],[],null,["# ee.ConfusionMatrix.array\n\nReturns a confusion matrix as an Array.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|---------------------------|---------|\n| ConfusionMatrix.array`()` | Array |\n\n| Argument | Type | Details |\n|-------------------------|-----------------|---------|\n| this: `confusionMatrix` | ConfusionMatrix | |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// Construct a confusion matrix from an array (rows are actual values,\n// columns are predicted values). We construct a confusion matrix here for\n// brevity and clear visualization, in most applications the confusion matrix\n// will be generated from ee.Classifier.confusionMatrix.\nvar array = ee.Array([[32, 0, 0, 0, 1, 0],\n [ 0, 5, 0, 0, 1, 0],\n [ 0, 0, 1, 3, 0, 0],\n [ 0, 1, 4, 26, 8, 0],\n [ 0, 0, 0, 7, 15, 0],\n [ 0, 0, 0, 1, 0, 5]]);\nvar confusionMatrix = ee.ConfusionMatrix(array);\nprint(\"ee.ConfusionMatrix\", confusionMatrix);\n\nprint(\"ee.ConfusionMatrix as ee.Array\", confusionMatrix.array());\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\nfrom pprint import pprint\n\n# Construct a confusion matrix from an array (rows are actual values,\n# columns are predicted values). We construct a confusion matrix here for\n# brevity and clear visualization, in most applications the confusion matrix\n# will be generated from ee.Classifier.confusionMatrix.\narray = ee.Array([[32, 0, 0, 0, 1, 0],\n [ 0, 5, 0, 0, 1, 0],\n [ 0, 0, 1, 3, 0, 0],\n [ 0, 1, 4, 26, 8, 0],\n [ 0, 0, 0, 7, 15, 0],\n [ 0, 0, 0, 1, 0, 5]])\nconfusion_matrix = ee.ConfusionMatrix(array)\nprint(\"ee.ConfusionMatrix:\")\npprint(confusion_matrix.getInfo())\n\nprint(\"ee.ConfusionMatrix as ee.Array:\")\npprint(confusion_matrix.array().getInfo())\n```"]]
