In [5]:

    

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn import metrics
from sklearn import datasets
from sklearn import tree


    

In [6]:

    

iris = datasets.load_iris()


    

In [7]:

    

x = iris.data[:,2:]
y = iris.target


    

In [9]:

    

train_x, test_x, train_y, test_y = train_test_split(x, y, train_size = .75, test_size = .25)
forest = RandomForestClassifier(n_estimators = 5, random_state = 2)
forest.fit(train_x, train_y)


    

    
Out[9]:





RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,
            oob_score=False, random_state=2, verbose=0, warm_start=False)

In [10]:

    

#Training score
forest.score(train_x, train_y)


    

    
Out[10]:





0.9821428571428571

In [11]:

    

#Test score
forest.score(test_x, test_y)


    

    
Out[11]:





0.94736842105263153