Google Earth Engine: Evaluate classifier of output type regression

Question

Background

According to the Google Earth Engine documentation for supervised classification, the accuracy assessment of classifiers such as ee.Classifier.smileRandomForest can be done using a confusionMatrix():

// Make a Random Forest classifier and train it.
var classifier = ee.Classifier.smileRandomForest(10)
    .train({
      features: training,
      classProperty: 'Land_Cover_Type_1',
      inputProperties: ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']
    });

// Classify the input imagery.
var classified = input.classify(classifier);

// Get a confusion matrix representing resubstitution accuracy.
var trainAccuracy = classifier.confusionMatrix();
print('Resubstitution error matrix: ', trainAccuracy);

However, I can't seem to find information on assessing the accuracy of regression output models in Google Earth Engine, such as ee.Classifier.libsvm.

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Question

How do I conduct accuracy assessment in Earth Engine for regression models? Example:

var classifier = ee.Classifier.libsvm({
    svmType: "EPSILON_SVR",
    kernelType: "POLY",
    shrinking: true,
    degree: 3,
    cost: 1,
    terminationEpsilon: 0.001,
    lossEpsilon: 0.1})
  .setOutputMode("REGRESSION")

Answer 1

Unfortunately, there is not yet such function in GEE; however, you could simply write the formula of RMSE or MAE in GEE.

As an example for RMSE calculation;

var observation = ee.Array([0.1,0.2,0.3,0.9])                                   
var prediction = ee.Array([0.12,0.24,0.31,0.89])

print(observation.subtract(prediction).pow(2).reduce('mean', [0]).sqrt(),'RMSE')