cart_classifier.py

import ee 
from ee_plugin import Map 

# Input imagery is a cloud-free Landsat 8 composite.
l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1')

image = ee.Algorithms.Landsat.simpleComposite(**{
  'collection': l8.filterDate('2018-01-01', '2018-12-31'),
  'asFloat': True
})

# Use these bands for prediction.
bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11']

# Load training points. The numeric property 'class' stores known labels.
points = ee.FeatureCollection('GOOGLE/EE/DEMOS/demo_landcover_labels')

# This property of the table stores the land cover labels.
label = 'landcover'

# Overlay the points on the imagery to get training.
training = image.select(bands).sampleRegions(**{
  'collection': points,
  'properties': [label],
  'scale': 30
})

# Train a CART classifier with default parameters.
trained = ee.Classifier.smileCart().train(training, label, bands)

# Classify the image with the same bands used for training.
classified = image.select(bands).classify(trained)

# Display the inputs and the results.
Map.centerObject(points, 11)
Map.addLayer(image, {'bands': ['B4', 'B3', 'B2'], 'max': 0.4}, 'image')
Map.addLayer(classified,
             {'min': 0, 'max': 2, 'palette': ['red', 'green', 'blue']},
             'classification')