Difference between GEE and sklearn random forest output

Question

I am training a random forest in GEE to predict canopy cover. See here for example. Implementation of RF is

var rf_model = ee.Classifier.randomForest(5).train(to_TrainAll, target, bands);

My predicted output mean value is low (expected ~20%, predicted ~8%) so I exported the training data and estimated using the sklearn implementation of RF and this returned a more realistic value. Training data available here.

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor

df = pd.read_csv('/Users/phil/Google_Drive/training_data_100.csv')
bands = ['B5','B7','B10', 'B11_mean','B1_variance3','B2_variance3','B8_variance3',
         'B8A_variance3','B12_variance3','B2_variance5','B8_variance5','B9_variance5',
         'B10_variance5', 'ndvi','ndvi_stdDev_5','ndvi_temporal_variance',
         'slope','aspect','precipitation','tavg_min','tavg_max']

output = []

for _ in range(10):
    df.loc[:, 'train'] = np.random.random(size=len(df)) < .95
    X = df[df.train].cc.values.reshape(-1, 1)
    Y = df[df.train][bands].values
    rf = RandomForestRegressor(5).fit(Y, X)
    output.append(rf.predict(df[~df.train][bands]).mean())

print 'actual: {:.2f} predicted: {:.2f}'.format(df.cc.mean(), np.mean(output))

actual: 20.15 predicted: 20.37

Also if I run

ee.Classifier.cart().train(to_TrainAll, target, bands);

I get a more realistic value. What am I doing wrong?

Answer 1

maybe it helps to specify ''setOutputMode'' to "regression" as done here https://doi.org/10.3390/rs10081167

Answer 2

This is obviously coming too late to be helpful, but for future visitors to this question I figured I'd add: EE's random forest classifier is not set up to handle regression at all. It's just a classifier. From my experience with it, I would guess that it's giving you such off-base values because it's trying to squeeze your continuous variable into series of unrelated classes, which is of course not what you're asking it to do. Hence, predictions that make no sense.