def logisticRegDemo(conn):
'''
Demonstrate Logistic Regression
'''
#1) Logistic Regression with Numeric Variables Alone
log_reg = LogisticRegression(conn)
#Train Model
mdl_dict, mdl_params = log_reg.train('public.wine_bool_training_set',
'indep', 'quality_label')
#Show Model Parameters
mdl_params.head()
#2) Logistic Regression Prediction
predictions = log_reg.predict('wine_bool_test_set', '', None)
predictions.head()
#Display ROC Curve
actual = predictions.get('quality_label')
predicted = predictions.get('prediction')
ROCPlot('ROC curve Logistic Reg. on Continuous Features ',
['Logistic Regression'], actual, predicted)
# 2) Logistic Regression with mixture of numeric and categorical columns
mdl_dict, mdl_params = log_reg.train('public.auto_mpg_bool_train', [
'1', 'height', 'width', 'length', 'highway_mpg', 'engine_size', 'make',
'fuel_type', 'fuel_system'
], 'is_expensive')
predictions = log_reg.predict('auto_mpg_bool_test', 'is_expensive', None)
cols = conn.fetchColumns(cursor, ['is_expensive', 'prediction'])
actual = predictions.get('is_expensive')
predicted = predictions.get('prediction')
ROCPlot('ROC curve Logistic Reg. including categorical data',
['Logistic Regression'], actual, predicted)
