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Computes a 2D confusion matrix for a classifier based on its training data (e.g., resubstitution error). Axis 0 of the matrix corresponds to the input classes and axis 1 corresponds to the output classes. The rows and columns start at class 0 and increase sequentially up to the maximum class value, so some rows or columns might be empty if the input classes aren't 0-based or sequential.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-07-13 UTC."],[[["\u003cp\u003e\u003ccode\u003econfusionMatrix()\u003c/code\u003e computes a 2D confusion matrix, representing the performance of a classifier by comparing predicted versus actual class values from its training data.\u003c/p\u003e\n"],["\u003cp\u003eThe matrix axes correspond to input (rows) and output (columns) classes, starting at 0 and increasing sequentially.\u003c/p\u003e\n"],["\u003cp\u003eRows or columns may be empty if input classes aren't 0-based or sequential, reflecting potential gaps in the class range.\u003c/p\u003e\n"],["\u003cp\u003eThis function is called on a classifier object and returns a \u003ccode\u003eConfusionMatrix\u003c/code\u003e object which can be used for further analysis, such as calculating overall accuracy.\u003c/p\u003e\n"]]],[],null,["Computes a 2D confusion matrix for a classifier based on its training data (e.g., resubstitution error). Axis 0 of the matrix corresponds to the input classes and axis 1 corresponds to the output classes. The rows and columns start at class 0 and increase sequentially up to the maximum class value, so some rows or columns might be empty if the input classes aren't 0-based or sequential.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------|-----------------|\n| Classifier.confusionMatrix`()` | ConfusionMatrix |\n\n| Argument | Type | Details |\n|--------------------|------------|------------------------|\n| this: `classifier` | Classifier | The classifier to use. |\n\nExamples\n\nCode Editor (JavaScript) \n\n```javascript\n// A Sentinel-2 surface reflectance image, reflectance bands selected,\n// serves as the source for training and prediction in this contrived example.\nvar img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')\n .select('B.*');\n\n// ESA WorldCover land cover map, used as label source in classifier training.\nvar lc = ee.Image('ESA/WorldCover/v100/2020');\n\n// Remap the land cover class values to a 0-based sequential series.\nvar classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];\nvar remapValues = ee.List.sequence(0, 10);\nvar label = 'lc';\nlc = lc.remap(classValues, remapValues).rename(label).toByte();\n\n// Add land cover as a band of the reflectance image and sample 100 pixels at\n// 10 m scale from each land cover class within a region of interest.\nvar roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);\nvar sample = img.addBands(lc).stratifiedSample({\n numPoints: 100,\n classBand: label,\n region: roi,\n scale: 10,\n geometries: true\n});\n\n// Add a random value field to the sample and use it to approximately split 80%\n// of the features into a training set and 20% into a validation set.\nsample = sample.randomColumn();\nvar trainingSample = sample.filter('random \u003c= 0.8');\nvar validationSample = sample.filter('random \u003e 0.8');\n\n// Train a 10-tree random forest classifier from the training sample.\nvar trainedClassifier = ee.Classifier.smileRandomForest(10).train({\n features: trainingSample,\n classProperty: label,\n inputProperties: img.bandNames()\n});\n\n// Get information about the trained classifier.\nprint('Results of trained classifier', trainedClassifier.explain());\n\n// Get a confusion matrix and overall accuracy for the training sample.\nvar trainAccuracy = trainedClassifier.confusionMatrix();\nprint('Training error matrix', trainAccuracy);\nprint('Training overall accuracy', trainAccuracy.accuracy());\n\n// Get a confusion matrix and overall accuracy for the validation sample.\nvalidationSample = validationSample.classify(trainedClassifier);\nvar validationAccuracy = validationSample.errorMatrix(label, 'classification');\nprint('Validation error matrix', validationAccuracy);\nprint('Validation accuracy', validationAccuracy.accuracy());\n\n// Classify the reflectance image from the trained classifier.\nvar imgClassified = img.classify(trainedClassifier);\n\n// Add the layers to the map.\nvar classVis = {\n min: 0,\n max: 10,\n palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',\n 'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']\n};\nMap.setCenter(-122.184, 37.796, 12);\nMap.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 3500}, 'img');\nMap.addLayer(lc, classVis, 'lc');\nMap.addLayer(imgClassified, classVis, 'Classified');\nMap.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);\nMap.addLayer(trainingSample, {color: 'black'}, 'Training sample', false);\nMap.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);\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\nColab (Python) \n\n```python\n# A Sentinel-2 surface reflectance image, reflectance bands selected,\n# serves as the source for training and prediction in this contrived example.\nimg = ee.Image(\n 'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'\n).select('B.*')\n\n# ESA WorldCover land cover map, used as label source in classifier training.\nlc = ee.Image('ESA/WorldCover/v100/2020')\n\n# Remap the land cover class values to a 0-based sequential series.\nclass_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]\nremap_values = ee.List.sequence(0, 10)\nlabel = 'lc'\nlc = lc.remap(class_values, remap_values).rename(label).toByte()\n\n# Add land cover as a band of the reflectance image and sample 100 pixels at\n# 10 m scale from each land cover class within a region of interest.\nroi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)\nsample = img.addBands(lc).stratifiedSample(\n numPoints=100, classBand=label, region=roi, scale=10, geometries=True\n)\n\n# Add a random value field to the sample and use it to approximately split 80%\n# of the features into a training set and 20% into a validation set.\nsample = sample.randomColumn()\ntraining_sample = sample.filter('random \u003c= 0.8')\nvalidation_sample = sample.filter('random \u003e 0.8')\n\n# Train a 10-tree random forest classifier from the training sample.\ntrained_classifier = ee.Classifier.smileRandomForest(10).train(\n features=training_sample,\n classProperty=label,\n inputProperties=img.bandNames(),\n)\n\n# Get information about the trained classifier.\ndisplay('Results of trained classifier', trained_classifier.explain())\n\n# Get a confusion matrix and overall accuracy for the training sample.\ntrain_accuracy = trained_classifier.confusionMatrix()\ndisplay('Training error matrix', train_accuracy)\ndisplay('Training overall accuracy', train_accuracy.accuracy())\n\n# Get a confusion matrix and overall accuracy for the validation sample.\nvalidation_sample = validation_sample.classify(trained_classifier)\nvalidation_accuracy = validation_sample.errorMatrix(label, 'classification')\ndisplay('Validation error matrix', validation_accuracy)\ndisplay('Validation accuracy', validation_accuracy.accuracy())\n\n# Classify the reflectance image from the trained classifier.\nimg_classified = img.classify(trained_classifier)\n\n# Add the layers to the map.\nclass_vis = {\n 'min': 0,\n 'max': 10,\n 'palette': [\n '006400',\n 'ffbb22',\n 'ffff4c',\n 'f096ff',\n 'fa0000',\n 'b4b4b4',\n 'f0f0f0',\n '0064c8',\n '0096a0',\n '00cf75',\n 'fae6a0',\n ],\n}\nm = geemap.Map()\nm.set_center(-122.184, 37.796, 12)\nm.add_layer(\n img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'\n)\nm.add_layer(lc, class_vis, 'lc')\nm.add_layer(img_classified, class_vis, 'Classified')\nm.add_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)\nm.add_layer(training_sample, {'color': 'black'}, 'Training sample', False)\nm.add_layer(\n validation_sample, {'color': 'white'}, 'Validation sample', False\n)\nm\n```"]]