Extension to sklearn.metrics to allow metrics with multiple predictions.
Project description
toppred
Extension to sklearn.metrics to allow metrics for classifiers that output a top n
prediction.
Some classifiers output confidence levels for each class.
Oftentimes, you want to evaluate the performance of such classifiers assuming the correct prediction is the top n
predictions with the highest confidence level.
This library serves as an extension to the functions provided by sklearn.metrics to allow for evaluating classifiers that do not output a single prediction per sample, but rather a range of top predictions per sample.
Installation
The most straightforward way of installing toppred
is via pip:
pip3 install toppred
Documentation
We provide an extensive documentation including installation instructions and reference at toppred.readthedocs.io.
Project details
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 toppred-0.0.3.tar.gz
.
File metadata
- Download URL: toppred-0.0.3.tar.gz
- Upload date:
- Size: 6.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/5.0.0 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
f5e4f5b059708b7e1a48e32752f19f22a410715846e61badb8d6c3d4cedebd85
|
|
MD5 |
e322038f5b4ac32a1aa307c52172edbf
|
|
BLAKE2b-256 |
66fb85f7ee212091178bb102eeda0581ac50facbb9b54e8a391ceb6d48962126
|
File details
Details for the file toppred-0.0.3-py3-none-any.whl
.
File metadata
- Download URL: toppred-0.0.3-py3-none-any.whl
- Upload date:
- Size: 7.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/5.0.0 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
5cc1f582e58852bee323ed7bcb59d9936dc3834789ef3fab9c5318dc9947ad0b
|
|
MD5 |
4b21260d794eced55b2a771047991a39
|
|
BLAKE2b-256 |
342b7c108a207c339dd44a1a33e930f45d4ad59feff0ac9317a790b291021b51
|