Interpretable rule ensembles via gradient boosting
Project description
ruleboost
Learn additive rule ensembles via gradiant boostig.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ruleboost-0.1.0.tar.gz
(2.5 kB
view details)
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 ruleboost-0.1.0.tar.gz.
File metadata
- Download URL: ruleboost-0.1.0.tar.gz
- Upload date:
- Size: 2.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
543aabd29f0a873e98b359239d69d52ec185e14d60f987a1fb6d7a54586b74a2
|
|
| MD5 |
a204ed1c42cae490402f7679037539b9
|
|
| BLAKE2b-256 |
b1f6896624911c4079659041bbcc5dffe695cf2a2461ec43b5ba47f604b09847
|
File details
Details for the file ruleboost-0.1.0-py3-none-any.whl.
File metadata
- Download URL: ruleboost-0.1.0-py3-none-any.whl
- Upload date:
- Size: 2.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
35ba1335c4e80a7e0817f52dac62fd4ae396c1fd81a07f49f16059bb497ec533
|
|
| MD5 |
1070e7f9ff787baa5986da4572ab9e1b
|
|
| BLAKE2b-256 |
dd1026afd93f44f29c4282f1b81a41c9aab4a44a658920e65d26ce36634760d0
|
