classification.js

var startDate = ee.Date('2016-01-01');
var endDate   = ee.Date('2016-12-31');
var Bounds = ee.FeatureCollection('ft:...');
var poly=ee.FeatureCollection('ft:...');


var L8 = ee.ImageCollection('LANDSAT/LC8_L1T');
var composite = ee.Algorithms.Landsat.simpleComposite({
  collection: L8.filterDate(startDate, endDate).filterBounds(Bounds),
  asFloat: true});

var bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11'];
var image = composite.select(bands).clip(Bounds);


var train = image.sampleRegions({
  collection: poly,
  properties: ['CLASS'],
  scale: 30  
});

// Train the classifier.
var trained = ee.Classifier.cart().train(train, 'CLASS', bands);
// Classify the image.
var classified = image.classify(trained);
print(classified)
// Create a palette to display the classes.
//var palette =['006400', '32CD32', 'EEE8AA',
//              '8B4513', '98FB98', '00FA9A',
//              '90EE90', '00008B', 'FF8C00',
//              'ADFF2F', '808080'];

// Display the classification result and the input image.

Map.addLayer(image, {bands: ['B4', 'B3', 'B2'], max: 0.5, gamma: 2});
Map.addLayer(classified, {min: 0, max: 20}, 'Vegetation Type');
  var tac = trained.confusionMatrix();
print('Resubstitution error matrix: ', tac);
print('Training overall accuracy: ', tac.accuracy());
print('Training kappa: ', tac.kappa());