SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
Project description
This Python package provides utilities to make it easier to run machine learning experiments with scikit-learn.
Command-line Interface
run_experiment is a command-line utility for running a series of learners on datasets specified in a configuration file. For more information about using run_experiment (including a quick example), go here.
Python API
If you just want to avoid writing a lot of boilerplate learning code, you can use our simple Python API. The main way you’ll want to use the API is through the load_examples function and the Learner class. For more details on how to simply train, test, cross-validate, and run grid search on a variety of scikit-learn models see the documentation.
A Note on Pronunciation
SciKit-Learn Laboratory (SKLL) is pronounced “skull”: that’s where the learning happens.
Requirements
Python 2.7+
Grid Map (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
configparser (only required for Python 2.7)
futures (only required for Python 2.7)
logutils (only required for Python 2.7)
Talks
Changelog
See GitHub releases.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file skll-0.22.4.tar.gz.
File metadata
- Download URL: skll-0.22.4.tar.gz
- Upload date:
- Size: 77.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b11937b5b341bebaea942f6be88d90cc0317cc61155d1908a19aa82938840de8 |
|
| MD5 | 3762d64c3191aeb8049c8f88d9d340ed |
|
| BLAKE2b-256 | 529edf3b959db426916c0f4182a7b621244360c8b353fe704603ff2fa0b8f832 |
File details
Details for the file skll-0.22.4-py2.py3-none-any.whl.
File metadata
- Download URL: skll-0.22.4-py2.py3-none-any.whl
- Upload date:
- Size: 62.8 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 400ec8d7f84a95c424e94bda8b3183bde7d80d48eaa9f71bd7bfa8e13d0824a1 |
|
| MD5 | 6f7d031e94800ffbc10ea21816d81cc5 |
|
| BLAKE2b-256 | f16089c581d0120fe24d9b67ede0524735a1cd47ceb9a982eb5ab242e0cdc610 |
