fastautoml is powerful and computationally efficient Python library for automated machine learning, intended for data scientists and with the goal of maximize their productivity.

fastautoml requires:

• scikit-learn (>= 0.22)

If you already have a working installation of scikit-learn and pandas, the easiest way to install fastautoml is using pip:

pip install fastautoml

The following example shows how to compute an optimal model for the MNIST dataset included with scikit-learn.

from fastautoml.fastautoml import AutoClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

model = AutoClassifier()
model.fit(X_train, y_train)
print("Score:", model.score(X_test, y_test))

• User Guide
• Reference API (TBD)
• Examples of usage

R. Leiva and contributors. If you want to contribute to this project, please contact with the main author.

This project is licensed under the 3-Clause BSD license - see the LICENSE.md file for details.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732667 RECAP