In [58]:

    

# Logistic Regression
from sklearn import datasets
from sklearn import metrics
from sklearn.linear_model import LogisticRegression


    

In [59]:

    

# load the iris datasets
dataset = datasets.load_iris()


    

In [60]:

    

# fit a logistic regression model to the data
model = LogisticRegression()
model.fit(dataset.data, dataset.target)


    

    
Out[60]:





LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

In [61]:

    

coreml_model = coremltools.converters.sklearn.convert(model,["a", "b", "c", "d"],"y")


    

In [62]:

    

coreml_model.save('Iris.mlmodel')