Demonstrates propositions of supervised machine learning theories
Project description
smltheory
Demonstrates propositions of supervised machine learning theories.
Installation
$ pip install smltheory
Usage
smltheory can be used to demonstrate propositions of
supervised machine learning theories. Specifically, the functions
demonstrate the propositions of excess risk decomposition
and the bias-variance tradeoff.
For a demonstration of each supervised machine learning proposition, see whitepaper.
Contributing
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
License
smltheory was created by Sebastian Sciarra. It is licensed under the terms of the MIT license.
Credits
smltheory was created with cookiecutter and the py-pkgs-cookiecutter template.
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
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file smltheory-0.1.10.tar.gz.
File metadata
- Download URL: smltheory-0.1.10.tar.gz
- Upload date:
- Size: 3.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
153acccc59801037671d54e78c1283ff27312afe3013c5d9d3934c3c1df7ca9b
|
|
| MD5 |
765f3ee0a8ac0cb7d94763162bb949c2
|
|
| BLAKE2b-256 |
e568c2fe18c427513ac8db9d532b7daff082e9a5ef8c6c19a28f2e25c3c74e27
|
File details
Details for the file smltheory-0.1.10-py3-none-any.whl.
File metadata
- Download URL: smltheory-0.1.10-py3-none-any.whl
- Upload date:
- Size: 3.4 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a88919ce1a3f1ac210201f6c4228a23fab4c21ad1d9dc575476d53a75c6c09df
|
|
| MD5 |
7046662924d4d966d1b504b636c3fa39
|
|
| BLAKE2b-256 |
f3393e5ff5f14495b5a63507ba7bc79e446ba72f606eccacdb1f4dfec3b1a692
|
