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.
Release files for toppred 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| toppred-0.0.3.tar.gz | 6.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| toppred-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.6 kB
Release files / toppred-0.0.3.tar.gz
| Download URL | toppred-0.0.3.tar.gz |
|---|---|
| Size | 6.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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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
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Release files / toppred-0.0.3-py3-none-any.whl
| Download URL | toppred-0.0.3-py3-none-any.whl |
|---|---|
| Size | 7.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| 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
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