

In [13]:

    
from sklearn import datasets
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier

Testing out decision trees to see if they may be useful with these data.



In [14]:

    
x = [[0, 0], [1, 1]]
y = [0, 1]

clf = tree.DecisionTreeClassifier()
clf = clf.fit(x, y)



In [15]:

    
clf.predict([[2., 2.]])









    Out[15]:





array([1])



In [16]:

    
clf.predict_proba([[2., 2.]])









    Out[16]:





array([[ 0.,  1.]])



In [17]:

    
data = datasets.load_iris()



In [19]:

    
model = DecisionTreeClassifier()
model.fit(data.data, data.target)









    



DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')