def createAutoMLClassifier(params=None):
df_train, df_test = get_boston_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='classifer',
column_descriptions=column_descriptions)
ml_predictor.train(df_train)
#ml_predictor.train(data, model_names=['DeepLearningClassifier'])
# Available options are
# DeepLearningClassifier and DeepLearningRegressor
# XGBClassifier and XGBRegressor
# LGBMClassifer and LGBMRegressor
# CatBoostClassifier and CatBoostRegressor
ml_predictor.score(df_test, df_test.MEDV)
-AF: here are some major advantages and disadvantages I found:
pros:
+ easy to install
+ easy to get it to work and print some results
+ already have an internal implementation of feature importance.:
- actually the "Importance" part comes from sklearn feature_importance_
when available. However, they do have this other analysis where they
go through each feature and see how their variance affect the variance
of the predictions that could potentially used to define a custom
"importance" or sensitivity.
cons:
- lack of documentation and organization
- results are printed or to ".dill" file. Difficult to handle the results
Might not be the best option.
"""
df_train, df_test = get_boston_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
# ml_predictor.set_params_and_defaults(df_train, compare_all_models=True)
ml_predictor.train(df_train)
score = ml_predictor.score(df_test, df_test.MEDV)
print(score)
# ml_predictor.save()
def load_data(self):
self.df_train, self.df_test = get_boston_dataset()