Tired of engineering the data, analyzing it to make new features and training multiple models and then picking the best among them? No worries now! GML is here for you!
GML is an automatic machine learning and feature engineering library in python built on top of Multiple Machine Learning packages. with this library,you can find and fill the missing values in your data, encode them, generate new features from them, select the best features and 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. in AUTO Machine Learning, there would be two rounds, in first round all the models will compete for top 5 and after that in second round those top 5 will compete for number one spot. 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 network architectures, other machine learning algorithms and many more cool things!
pip install GML
* 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)
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: email@example.com
I haven't uploaded source code yet on this repo. will upload it later after writing comments.