ML VizKit

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Reusable visualizations for inspecting, comparing, and explaining trained machine-learning models.

ML VizKit provides high-level Python functions for common machine-learning visualizations. It works with trained models, predictions, and experiment results produced by libraries such as scikit-learn.

The package does not train models, select features, choose algorithms, or make analytical decisions.

Design

  • Accept already-trained models, predictions, or completed experiment results.
  • Reuse established visualization primitives from scikit-learn when they exist.
  • Add small higher-level visualizations where the underlying libraries do not.
  • Return Matplotlib Axes objects.
  • Never call plt.show().
  • Keep analytical choices visible to the caller.
  • Keep the implementation readable and replaceable.

Install

uv add ml-vizkit

Example

from ml_vizkit import show_confusion_matrix

ax = show_confusion_matrix(y_test, y_pred)
ax.set_title("Penguin Species Classification")

The caller controls display and composition. In a script, for example:

import matplotlib.pyplot as plt

ax = show_confusion_matrix(y_test, y_pred)
plt.show()

Initial API

Classification:

  • show_decision_boundary()
  • show_confusion_matrix()
  • show_prediction_errors()
  • show_class_distribution()

Regression:

  • show_actual_vs_predicted()
  • show_residuals()

Model inspection:

  • show_feature_importance()

Experiment inspection:

  • show_train_test_split()
  • compare_splits()
  • compare_models()

Developer Command Reference

Show command reference

In a machine terminal

Open a machine terminal where you want the project:

git clone https://github.com/pup-pack/pup-up

cd pup-up
code .

In a VS Code terminal

uv self update
uv python pin 3.14
uv python install
uv lock --upgrade
uv sync

uv run pre-commit install
uv run pre-commit autoupdate

git add -A
uv run pre-commit run --all-files
# repeat if changes were made
uv run pre-commit run --all-files

# run locally to test
uv run pup-up
uv run pup-up --diff
uv run pup-up --write
uv run pup-up --write .gitattributes .github/.yamllint.yml .github/workflows/links.yml

# types, tests, docs
uv run ty check
uv run python -m pytest
uv run python -m zensical build

# save progress
git add -A
git commit -m "update"
git push -u origin main

Documentation

Annotations

.annotations/annotations.md

Citation

CITATION.cff

License

MIT

Project links

Data verified by PyPI on Aug 15, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.0.2)

1 maintainer

PyPI data
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Avatar for denisecase from gravatar.com denisecase

Credits

Author: Denise Case

GitHub Statistics

Data verified by PyPI on Aug 15, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.14

Tags

data science explainable AI machine learning model interpretability visualization

Classifiers

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