creditriskutilities 1.0.2

Reusable credit risk model evaluation and data utility functions.

creditriskutilities

creditriskutilities is a lightweight Python package developed to streamline the evaluation and comparison of credit risk classification models. The package abstracts commonly repeated tasks in model assessment — such as computing classification metrics, visualizing feature importance, applying value mappings, and generating side-by-side model comparisons — into standalone, reusable functions. These functions are especially useful in credit analytics pipelines, where consistent and interpretable model evaluation is critical.

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Why This Package?

In credit risk analytics, we often work with:

• Encoded categorical data
• Tree-based models with feature importances
• A need to report multiple evaluation metrics
• Repetitive output saving and visualization logic

Instead of rewriting code across notebooks and scripts, creditriskutilities offers clean, modular tools you can plug directly into your pipeline.

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Key Features

• evaluate_model: Automatically compute and save accuracy, precision, recall, and F1 — plus a CSV report and confusion matrix plot.
• plot_feature_importance: Visualize top N features by importance and save plot + CSV.
• compare_models: Compare multiple model metrics in a single bar plot.
• apply_mappings: Map coded values to readable labels using a dictionary — great for pre-modeling pipelines.
• create_output_dir: Safely create folders as needed (used internally by other functions).

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Who Should Use This?

This package is ideal for:

• Students and teams building machine learning pipelines in finance
• Practitioners working with classification problems (especially in credit scoring)
• Developers who want to avoid re-writing evaluation logic in every notebook or script

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Authors

• Stallon Pinto
• Ayush Joshi
• Zhanerke Zhumash