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State-of-the art Automated Machine Learning python library for Tabular Data

Project description

AutoML Alex

Downloads PyPI - Python Version PyPI CodeFactor Telegram License


State-of-the art Automated Machine Learning python library for Tabular Data

bench

From AutoML-Benchmark

Scheme

scheme

Features

  • Automated Data Clean (Auto Clean)
  • Automated Feature Engineering (Auto FE)
  • Smart Hyperparameter Optimization (HPO)
  • Feature Generation
  • Feature Selection
  • Models Selection
  • Cross Validation
  • Timelimit and EarlyStoping

Installation

pip install automl-alex

🚀 Examples

Classifier:

from automl_alex import AutoMLClassifier

model = AutoMLClassifier(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)

Regression:

from automl_alex import AutoMLRegressor

model = AutoMLRegressor(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)

More examples in the folder ./examples:

What's inside

It integrates many popular frameworks:

  • scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
  • Optuna
  • ...

Note:

  • With a large dataset, a lot of memory is required! Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
  • Do not work with timeseries and texts yet

Road Map

  • Feature Generation

  • Advanced Logging

  • New Features Generators

  • DL Encoders

  • Save/Load and Predict on New Samples

  • Add More libs (NNs)

  • Add opt Pruners

  • Build pipelines

  • Docs Site

Contact

Telegram Group

Project details


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