pip install auto-sklearn==0.14.0

Automated machine learning.

Source
Among top 2% packages on PyPI.
Over 80.1K downloads in the last 90 days.

Commonly used with auto-sklearn

Based on how often these packages appear together in public requirements.txt files on GitHub.

pynisher

A small Python library to limit the resources used by a function by executing it inside a subprocess.

smac

SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.

arff2pandas

A bidirectional converter arff to pandas.DataFrame

ConfigSpace

Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning.

idx2numpy

A Python package which provides tools to convert files to and from IDX format (described at http://yann.lecun.com/exdb/mnist/) into numpy.ndarray.

yasm

Python State Machines for Humans

sheepts

Light Time Series Toolbox

pyrfr

None

root-numpy

The interface between ROOT and NumPy

geneimpacts

normalize effects from variant annotation tools (snpEff, VEP)

inheritance

inheritance models for mendelian genetics

tfa-nightly

TensorFlow Addons.

gemini

A database framework for exploring genetic variation

openml

Python API for OpenML

bx-python

Tools for manipulating biological data, particularly multiple sequence alignments

nosenicedots

Nose plugin that prints nicer dots grouped by class/module.

rhsm

A Python library to communicate with a Red Hat Unified Entitlement Platform

topiary

Predict cancer epitopes from cancer sequence data

vcf-annotate-polyphen

a tool to annotate human VCF files with PolyPhen2 effect measures

Version usage of auto-sklearn

Proportion of downloaded versions in the last 3 months (only versions over 1%).

0.14.0

22.25%

0.13.0

21.41%

0.12.7

13.01%

0.12.6

4.26%

0.11.1

2.76%

0.12.0

2.67%

0.12.3

2.46%

0.12.5

1.98%

0.10.0

1.84%

0.12.4

1.81%

0.12.1

1.72%

0.12.2

1.68%

0.8.0

1.60%

0.6.0

1.46%

0.11.0

1.46%

0.9.0

1.42%

0.5.2

1.42%

0.7.0

1.36%

0.7.1

1.31%

0.5.0

1.21%

0.5.1

1.20%

0.4.2

1.18%

0.4.1

1.16%

0.4.0

1.15%

0.3.0

1.14%