In [2]:

    

import graphlab
graphlab.canvas.set_target('ipynb')

x = graphlab.SFrame({'x1':[1,0,1,0],'x2':['1','1','0','0'],'x3':['1','0','1','1'],'y':['1','-1','-1','1']})
x


    

    
Out[2]:





    

        
x1
        
x2
        
x3
        
y
    
    

        
1
        
1
        
1
        
1
    
    

        
0
        
1
        
0
        
-1
    
    

        
1
        
0
        
1
        
-1
    
    

        
0
        
0
        
1
        
1
    

[4 rows x 4 columns]

In [3]:

    

features = ['x1','x2','x3']
target = 'y'   

decision_tree_model = graphlab.decision_tree_classifier.create(x, validation_set=None,
                                target = target, features = features)


    

    





WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.






    





Decision tree classifier:






    





--------------------------------------------------------






    





Number of examples          : 4






    





Number of classes           : 2






    





Number of feature columns   : 3






    





Number of unpacked features : 3






    





+-----------+--------------+-------------------+-------------------+






    





| Iteration | Elapsed Time | Training-accuracy | Training-log_loss |






    





| 1         | 0.001000     | 1.000000          | 0.634946          |






    





+-----------+--------------+-------------------+-------------------+

In [4]:

    

decision_tree_model.show()


    

In [11]:

    

decision_tree_model.show(view="Tree")


    

In [14]:

    

# Accuracy 
print decision_tree_model.evaluate(x)['accuracy']


    

    




1.0