A framework for fast and interactive conducting machine learning experiments on tabular data
An interactive environment for modular feature engineering, experiment tracking, feature selection and stacking.
Install KTS with pip install kts. Compatible with Python 3.6+.
Define features as independent blocks to organize your projects.
Track source code of every feature and experiment to make each of them reproducible.
Compute independent features in parallel. Cache them to avoid repeated computations.
Track your progress with local leaderboards.
Compute feature importances and select features from any experiment
with experiment.feature_importances() and experiment.select().
Monitor the progress of everything going on in KTS with our interactive reports.
From model fitting to computing feature importances.
Start exploring KTS with tutorial based on Titanic dataset. Run notebooks interactively in Binder or just read them in NBViewer.
Check out docs.kts.ai for a more detailed description of KTS features and interfaces
Most of our functions and classes have rich docstrings. Read them right in your notebook, without interruption.
MVP of the project was designed and implemented by the team of Mikhail Andronov, Roman Gorb and Nikita Konodyuk under the mentorship of Alexander Avdyushenko during a project practice held by Yandex and Higher School of Economics on 1-14 February 2019 at Educational Center «Sirius».
FAQs
A framework for fast and interactive conducting machine learning experiments on tabular data
We found that kts demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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