RF vs MLP sandbox for learning the "if" function

gistfile1.txt

import numpy as np
import sklearn.ensemble
from sklearn.neural_network import MLPClassifier

X = np.random.uniform(low=0, high=5000000,size=(10000,2))
y = np.array(X[:,0] > X[:,1],dtype=int)
X_test = np.random.uniform(low=0,high=5000000,size=(10000,2))
y_test = np.array(X_test[:,0] > X_test[:,1],dtype=int)

clf_rf = sklearn.ensemble.RandomForestClassifier(n_estimators=50,criterion='entropy', max_depth=2)
clf_rf.fit(X,y)

clf_mlp = MLPClassifier(solver='adam', activation='relu',alpha=1e-5, hidden_layer_sizes=(1), random_state=1, max_iter=1000)
clf_mlp.fit(X,y)

print "RF  SCORE :",clf_rf.score(X_test,y_test)
print "MLP SCORE :",clf_mlp.score(X_test,y_test)