The motivation of this repository is to show the result of auto-sklearn examples.
You can install auto-sklearn and related libraries with the following command:
pip install -r requirements.txt
auto-sklearn would be super useful to train better models without thinking feature preprocessing and algorithms carefully. It is basically implemented on top of the scikit-learn pipeline interface.
- auto-sklearn allows us to train models using cross-validation simply.
- auto-sklearn supports a bunch of metrics for both of classification and regression.
- auto-sklearn allows us to train models in parallel using
multiprocessinglibrary. - auto-sklearn allows us to do one-hot-encoding and some feature preprocessing automatically setting each attribute type to numerical or categorical.
What is auto-sklearn? — AutoSklearn 0.2.0 documentation
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator:
>>> import autosklearn.classification
>>> cls = autosklearn.classification.AutoSklearnClassifier()
>>> cls.fit(X_train, y_train)
>>> predictions = cls.predict(X_test, y_test)
auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Learn more about the technology behind auto-sklearn by reading this paper published at the NIPS 2015.