Automatic Machine Learning with many powerful tools.
GML - Ghalat Machine Learning!
Tired of training multiple models and then picking the best among them? No worries now! GML is here for you!
GML is an automatic machine learning library in python built on top of Scikit-Learn,Keras,XGBoost,LightGBM and Catboost. with this library, you can train your data on multiple machine learning algorithms and a neural network! not only training but scaling the data for normal distribution and after scaling and training, testing the data on validation data (don't worry you don't need to provide validation data. we will extract it from your data). after testing models on validation data, they will be ranked accordingly and you will see which one performs better than other. the first ranked model will be returned (untrained, so you can train it yourself and check results). You already got some models? no problem! pass them to us to make them compete with our models and let see who wins ;-)
In future updates many other things will also be automated like hyper parameter tunning, multiple neural networks, other machine learning algorithms and many more cool things!
See GML in Action!!
- For Classification tasks GML Classifier
- For Regression tasks GML Regressor
These parameters are common in both GMLRegressor and GMLClassifier
* X Data column excluding the target column. it can either be a pandas dataframe or a numpy array. but please make sure your data doesn't contains missing data or non-numeric data. (clean it before passing) * y The targeted column
Below parameters are optional.
* metric metric on which you want to test your model. by default, it is mean-squared-error for regression and accuracy score for classification * test_Size size to split your test data, by default = 0.3 (70% training 30% testing) * folds (only in GMLClassifier) Data will also be validated using KFolds. pass number of folds. by default folds = 5 * shuffle Shuffle the data when spliting for validation. by default = True * scaler for Scaler pass: 'SS' for StandardScalar 'MM' for MinMaxScalar 'log' for Log scalar None for not scaling by default: StandardScalar * models You got your own models to make them compete with our models? pass them in a list here. default = None * neural_net Want to train on Neural Networks? Pass 'Yes', default = 'No' * epochs for neural networks, by default = 10 * verbose for neural networks, by default = True
Parameter when creating object of GML
models = Ghalat_Machine_Learning(n_estimators=300)
- by default n_estimators are 300, you can change it to whatever you want.
As its first version of GML, feel free to give suggestions,ask questions,report bugs etc in issues portion of this repository!
you can directly contact me at: <font color="blue"> firstname.lastname@example.org</font>
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