#NEWLINE# def __init__( self , n_estimators = 10 , max_depth = 5 , min_samples_split = 2 , min_samples_leaf = 1 , min_weight_fraction_leaf = 0.0 , max_leaf_nodes = None , sparse_output = True , n_jobs = 1 , random_state = None , verbose = 0 , warm_start = False ) : #NEWLINE# #INDENT# super ( RandomTreesEmbedding , self ) . __init__ ( base_estimator = ExtraTreeRegressor ( ) , n_estimators = max_leaf_nodes , estimator_params = ( 'criterion' , 'max_depth' , 'min_samples_split' , 'min_samples_leaf' , 'min_weight_fraction_leaf' , 'max_features' , 'max_leaf_nodes' , 'random_state' ) , bootstrap = False , oob_score = False , n_jobs = n_jobs , random_state = random_state , verbose = verbose , warm_start = warm_start ) #NEWLINE# self . criterion = 'mse' #NEWLINE# self . max_depth = max_depth #NEWLINE# self . min_samples_split = min_samples_split #NEWLINE# self . min_samples_leaf = min_samples_leaf #NEWLINE# self . min_weight_fraction_leaf = min_weight_fraction_leaf #NEWLINE# self . max_features = 1 #NEWLINE# self . max_leaf_nodes = max_leaf_nodes #NEWLINE# self . sparse_output = sparse_output