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# import Logistic Regression
from sklearn.linear_model import LogisticRegression

# create logistic regression model
lr = LogisticRegression(C=1.0, random_state=0)
# C is inverse regulation parameter (smaller the stronger)

# train the model
lr.fit(X_train_std, y_train)

# predict the probabilities of the first sample
lr.predict_proba(X_test_std[0,:])

# plot using user-defined function
plot_decision_regions(X_combined_std,
                      y_combined, classifier=lr,
                      test_idx=range(105,150))

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()