Complete Confusion
A simple call to visualize performance metrics for classification and regression models.
Installation
pip install complete-confusion
Usage
import complete_confusion as cc
# Example data
predictions = [0, 1, 0, 2, 1, 2, 0]
trues = [0, 1, 0, 2, 0, 2, 2]
classes = ["Class 0", "Class 1", "Class 2"]
output_path = "confusion_matrix.html"
cc.export_confusion_matrix_to_html(predictions, trues, classes, output_path)
Development
poetry install
poetry shell
python evaluate.py
Metadata
Release files for complete-confusion 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| complete_confusion-0.1.0.tar.gz | 7.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| complete_confusion-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.1 kB
Release files / complete_confusion-0.1.0.tar.gz
| Download URL | complete_confusion-0.1.0.tar.gz |
|---|---|
| Size | 7.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / complete_confusion-0.1.0-py3-none-any.whl
| Download URL | complete_confusion-0.1.0-py3-none-any.whl |
|---|---|
| Size | 9.2 kB |
| Tags | Python 3 |
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poetry/1.8.3 CPython/3.9.13 Darwin/23.5.0
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