code: from sklearn.ensemble import AdaBoostClassifier
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.datasets import make_gaussian_quantiles
  from sklearn.ensemble import RandomForestClassifier
  
  def AdaBoost(X_train, y_train, X_test, DEPTH, N_ESTIMATORS):
  	# Create and fit an AdaBoosted decision tree
  	bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=DEPTH),
  			         algorithm="SAMME",
  			         n_estimators=N_ESTIMATORS)
  
  	bdt.fit(X_train, y_train)
  
  	# Test classifier on test data
  	y_out = bdt.predict(X_test)
  	return y_out
  
  def ForestIB(X_train, y_train, X_test, DEPTH, N_ESTIMATORS):
  
  	# Create the random forest object which will include all the parameters
  	# for the fit
  	forest = RandomForestClassifier(n_estimators = N_ESTIMATORS, max_depth=DEPTH, max_features=DEPTH, criterion='gini', n_jobs=-1)
  
  	# Fit the training data to the Survived labels and create the decision trees
  	forest = forest.fit(X_train, y_train)
  
  	# Take the same decision trees and run it on the test data
  	y_out = forest.predict(X_test)
  	return y_out
  
apis: sklearn.tree.DecisionTreeClassifier sklearn.ensemble.RandomForestClassifier