decima2 0.4.1

Evaluation Toolkit for Machine Learning Models

Decima2 AI Evaluation Toolkit

Introduction

Welcome to the Decima2 AI Evaluation Toolkit — a comprehensive suite of tools designed to empower developers with the insights needed to effectively evaluate and enhance machine learning models. Our toolkit focuses on making complex concepts in machine learning intuitive and accessible, allowing users to derive meaningful insights without the steep learning curve often associated with advanced analytics.

Table of Contents

1. Installation
2. Model Tools
   • Model Feature Importance
   • Grouped Feature Importance
3. Data Tools
4. Outcome Tools
   • Individual NLP Explanation
   • Individual Feature Importance
5. License
6. Contributing
7. Contact

Installation

You can install the package using pip:

pip install decima2

Model Tools

Gain insights into how your models make predictions with clear, interpretable visualizations and explanations, making it easier to communicate results.

Model Feature Importance

For Tabular Data

This tool allows users to examine which features were most important for their model's perfomance. Given a numerical dataset and pre-trained model, the model_feature_importance module returns either a textual or graphical representation of which features were most important.

Instructions

For detailed usage instructions and to explore how the module works check out our Developer Docs

Tutorial

To explore tutorials on model feature importance including motivations and comparisons with SHAP check out our Jupyter Notebooks

Grouped Feature Importance

For Tabular Data

from decima2 import grouped_feature_importance

This tool builds on model_feature_importance to give users an insight into which features were most influential for their model over a select group of data. For example, a user may want to compare the most important feature across men and women in their data, or people with an income over 65k and under 65k.

Instructions

For detailed usage instructions and to explore how the module works check out our Developer Docs

Tutorial

To explore tutorials on grouped feature importance including motivation and use-cases check out our Jupyter Notebooks

Data Tools

Coming Soon

These tools help you evaluate your data

Outcome Tools

These tools help you to evaluate the outcomes of your model

Individual NLP Explanation

Understand which terms were most impactful in driving the similarity between two embedded texts

from decima2 import individual_nlp_explanation

This tool allows users to explore which terms were most influential in driving similarity score between the two texts in embedded space as determined by the user specified model.

Instructions

For detailed usage instructions and to explore how the module works check out our Developer Docs

Tutorial

To explore tutorials on individual nlp explanation and use-cases check out our Jupyter Notebooks

Individual Feature Importance

For Tabular Data

This tool allows users to examine which features were most important for an individual prediction. Given a numerical dataset, a pre-trained model and an instance to be explainaed, the individual_feature_importance module returns a graphical representation of which features were most important.

from decima2 import individual_feature_importance

Tutorial

To explore tutorials on individual feature importance and use-cases check out our Jupyter Notebooks

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please create a pull request or open an issue for any improvements, bugs, or feature requests.

Contact

For inquiries, please reach out to [e-mail]