Computes a 2D error matrix for a collection by comparing two columns of a collection: one containing the actual values, and one containing predicted values.The values are expected to be small contiguous integers, starting from 0. Axis 0 (the rows) of the matrix correspond to the actual values, and Axis 1 (the columns) to the predicted values.
Usage | Returns | FeatureCollection.errorMatrix(actual, predicted, order) | ConfusionMatrix |
Argument | Type | Details | this: collection | FeatureCollection | The input collection. |
actual | String | The name of the property containing the actual value. |
predicted | String | The name of the property containing the predicted value. |
order | List, default: null | A list of the expected values. If this argument is not specified, the values are assumed to be contiguous and span the range 0 to maxValue. If specified, only values matching this list are used, and the matrix will have dimensions and order matching the this list. |
Examples
Code Editor (JavaScript)
/**
* Classifies features in a FeatureCollection and computes an error matrix.
*/
// Combine Landsat and NLCD images using only the bands representing
// predictor variables (spectral reflectance) and target labels (land cover).
var spectral =
ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820').select('SR_B[1-7]');
var landcover =
ee.Image('USGS/NLCD_RELEASES/2016_REL/2016').select('landcover');
var sampleSource = spectral.addBands(landcover);
// Sample the combined images to generate a FeatureCollection.
var sample = sampleSource.sample({
region: spectral.geometry(), // sample only from within Landsat image extent
scale: 30,
numPixels: 2000,
geometries: true
})
// Add a random value column with uniform distribution for hold-out
// training/validation splitting.
.randomColumn({distribution: 'uniform'});
print('Sample for classifier development', sample);
// Split out ~80% of the sample for training the classifier.
var training = sample.filter('random < 0.8');
print('Training set', training);
// Train a random forest classifier.
var classifier = ee.Classifier.smileRandomForest(10).train({
features: training,
classProperty: landcover.bandNames().get(0),
inputProperties: spectral.bandNames()
});
// Classify the sample.
var predictions = sample.classify(
{classifier: classifier, outputName: 'predicted_landcover'});
print('Predictions', predictions);
// Split out the validation feature set.
var validation = predictions.filter('random >= 0.8');
print('Validation set', validation);
// Get a list of possible class values to use for error matrix axis labels.
var order = sample.aggregate_array('landcover').distinct().sort();
print('Error matrix axis labels', order);
// Compute an error matrix that compares predicted vs. expected values.
var errorMatrix = validation.errorMatrix({
actual: landcover.bandNames().get(0),
predicted: 'predicted_landcover',
order: order
});
print('Error matrix', errorMatrix);
// Compute accuracy metrics from the error matrix.
print("Overall accuracy", errorMatrix.accuracy());
print("Consumer's accuracy", errorMatrix.consumersAccuracy());
print("Producer's accuracy", errorMatrix.producersAccuracy());
print("Kappa", errorMatrix.kappa());