AutoEnsembler 1.3

This AutoEnsembler helps you to find the best Ensemble model for you

AutoEnsembler

This is an AutoEnsembler package, it helps you to find the best ensemble model for Classification and Regression problem. As we know that every model has its own speciality, by assuming this, I built AutoEnClassifer and AutoEnRegressor on top of LogisticRegression/Lasso, SVC/SVR, RandomForestClassifier/RandomForestRegressor, AdaBoostClassifer/AdaBoostRegressor and KNeighborsClassifier/KNeighborsRegressor.

What's new ?

• Bugs Fixed
• Time Optimized

Uniqueness

• In AutoEnClassifer, you can pass a parameter that you want to optimize, i.e. 'FN' / 'FP'
• While training, by default it will split the data into training data and validation data by 0.2 (you can also specify) and it will show you the accuracy_score/r2_score (with respect to each model you selected and of AutoEnClassifer/AutoEnRegressor) on validation data
• While initiating the model you can also specify which models should be used for ensembling and what type of search you want to use viz. GridSearchCV/RandomizedSearchCV

Motivation

I participated in various competitions and I learned many things from it. I wanted to share my knowledge in this amazing field of Data Science and Machine Learning. If you are beginner in this field, then try to compete this model with your model.

When to use ?

If you want to build Robust Model with mean less time.

Installation

 pip install AutoEnsembler

How to use ?

AutoEnClassifier

After installing, you can import as shown below. By default LogisticRegression/Lasso and RandomForestClassifier/RandomForestRegressor is selected. While fitting the model I passed 0.25 as validation_split (by default is 0.2). You can also see the accuracy_score/r2_score of individual model and of AutoEn model on validation_split data and you can also see the weight used for individual models for prediction. Note :- 1) Before fitting the data, do feature scaling. 2) (Recommended) Create your own validation data and this validation data should follow same distribution as test data for best results on test data.

Here, I specified to use all the models by passing True as a parameter with respect to each model name. By default False parameter is passed to GridSearch and it will use RandomizedSearchCV and also you can see in warnings.

Now, I enabled GridSearch and I passed own validation data to it and now the score will compute on validation_data.

As you can see I passed 'FP' as parameter to optimize, to optimizing the 'FP' as you can see below. Note:- Here 'FP' count is optimized with respect to validation data and on your test data it will be more or less equal, depending upon the size. You may think how it is optimized. while ensembling, you may get multiple same accuracy models, from that it will least select least 'FN'/'FP' as you specified

AutoEnRegressor

As you can see without doing much and with three models I reached near to 0.7 r2_score. Almost all features are similar with respect to AutoEnClassifier. Reminder :- (Recommended) Create your own validation data and this validation data should follow same distribution as test data for best results on test_data.

To Do

• To include xgboost and lightgbm model.
• To include scaling,class_weight attribute.

Bug / Feature Request

If you find a bug, kindly open an issue here by including your search query and the expected result. If you would like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Want to Contribute

If you are strong in OOPS concept/Machine Learning, please feel free to contact me. Email :- [e-mail]

Happy Learning!!