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What is PyCM?
PyCM offers a comprehensive solution for post-classification model assessment within the Python ecosystem. It functions as a multi-class confusion matrix library, adept at handling both input data vectors and direct matrix inputs. This tool provides extensive support for a wide range of classes and overall statistical parameters, making it highly versatile.
Designed primarily for data scientists, PyCM serves as a robust utility for evaluating the performance of diverse predictive models and classifiers. It facilitates in-depth analysis through its broad array of metrics, enabling accurate assessment and comparison of classification results. The library emphasizes ease of use and detailed reporting capabilities for model evaluation tasks.
Designed primarily for data scientists, PyCM serves as a robust utility for evaluating the performance of diverse predictive models and classifiers. It facilitates in-depth analysis through its broad array of metrics, enabling accurate assessment and comparison of classification results. The library emphasizes ease of use and detailed reporting capabilities for model evaluation tasks.
Features
- Multi-class Confusion Matrix Generation: Creates confusion matrices for evaluating models with multiple classes.
- Flexible Input Support: Accepts both input data vectors and direct matrix inputs.
- Extensive Statistical Parameters: Supports a wide range of class-specific and overall statistical metrics for evaluation.
- Python Library Integration: Easily integrates into Python workflows for data science and machine learning.
Use Cases
- Evaluating multi-class classification model performance.
- Comparing the effectiveness of different classifiers.
- Analyzing prediction errors in machine learning models.
- Generating detailed statistical reports for classification tasks.
- Post-classification analysis in data science projects.
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