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COMBAT Project Documentation

Welcome to the project documentation for COMBAT, a toy module designed to facilitate the PD (Probability of Default) development process.

Table Of Contents

  1. Motivation
  2. Problem Solved
  3. Tech Stack
  4. Design
  5. How to Run the Project

Motivation

The creation of the COMBAT Python package stemmed from a fundamental need within the field of risk management and predictive modeling. Here's a potential motivation for its creation:

In the realm of risk management and predictive modeling, there exists a growing demand for comprehensive, user-friendly tools that streamline the process of model development, validation, and deployment. Recognizing this need, we embarked on the creation of the COMBAT package.

Our motivation was driven by the desire to provide analysts, data scientists, and risk management professionals with a powerful toolkit that not only simplifies complex modeling tasks but also enhances efficiency and accuracy throughout the entire model development lifecycle.

In essence, the motivation behind the development of the COMBAT package is to empower users with a comprehensive suite of tools that streamline the process of risk modeling, from data preprocessing to model deployment. By providing a unified and user-friendly platform, we aim to democratize advanced analytics and facilitate informed decision-making in the field of risk management.

Problem Solved

We aimed to address several key challenges commonly faced by practitioners in this field, including:

  1. Complexity of Model Development: Building predictive models for risk assessment often involves intricate processes such as feature engineering, model selection, and calibration. COMBAT aims to simplify these tasks by offering intuitive functions and streamlined workflows.
  2. Integration of Multiple Libraries and Techniques: Leveraging the strengths of various libraries and techniques, such as scikit-learn and statsmodels, can significantly enhance model performance. COMBAT integrates these tools seamlessly, allowing users to harness the power of different methodologies within a unified framework.
  3. Efficient Ensemble Modeling: Ensemble techniques, such as bagging and stacking, have proven effective in improving model robustness and predictive accuracy. COMBAT provides built-in functionality for ensemble modeling, enabling users to effortlessly combine multiple models for superior performance.
  4. Model Interpretability: Understanding and interpreting model outputs are critical for effective decision-making and risk management. COMBAT includes tools for model validation, feature importance analysis, and calibration, empowering users to build transparent and reliable models.
  5. Scalability and Flexibility: As datasets grow in size and complexity, scalability becomes a crucial consideration. COMBAT is designed to handle large datasets efficiently while offering flexibility to accommodate diverse modeling requirements and business contexts.

In essense the entire process to develop a PD model could be reduced to 1-2 days.

Tech Stack

COMBAT leverages several key Python libraries essential for ML modeling, including:

  1. sklearn
  2. statsmodels
  3. scipy
  4. pandas
  5. numpy
  6. matplotlib

Design

The COMBAT framework comprises 6 modules:

  1. models: Includes the LogitModel class containing all necessary Logistic Regression features.
  2. combat: Contains functions for model building, testing, and aggregating into ensembles.
  3. calibration: Designed for performing calibration of the final PDs.
  4. short_list: Created to investigate the explanatory power of variables.
  5. transform: Built for WoE (Weight of Evidence) transformation of explanatory variables.
  6. scorecard: Designed to transform the final PD into a more convenient value.
  7. utilities: A set of utility functions.

How to Run the Project

The documentation adheres to best practices for project documentation and consists of three separate parts:

  1. Project Overview - Provides an introduction to the project, its objectives, and its intended audience. This section gives users a high-level understanding of what the project is about and what they can expect to find in the documentation

  2. Tutorials - Offers step-by-step guides and examples to help users learn how to use the project's features effectively. Tutorials provide hands-on experience and practical insights into using the project in real-world scenarios.

  3. How-To Guides - Provides in-depth instructions and explanations on how to accomplish specific tasks or solve common problems using the project. How-to guides offer detailed guidance and best practices to help users achieve their goals efficiently.

Quickly find what you're looking for depending on your use case by referencing the different pages.