Comparative Analysis of Interpretable Models for Mortgage Default Prediction

The timeframe for this project is within 3 months, and I expect both visualizations and a written report as the output.

Ideal skills and experience for this project include:
- Proficiency in data analysis and machine learning techniques
- Experience working with mortgage default data
- Ability to create visualizations and reports that clearly communicate insights and findings
- Strong attention to detail and ability to meet deadlines

Title: Comparative Analysis of Interpretable Models for Mortgage Default Prediction

Abstract:
This project aims to investigate and compare the effectiveness of various interpretability methodologies for mortgage default prediction. We will employ the expertise of data scientists to explore three different techniques: Partial Dependence Plots (PDPs), LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations). The project will involve analyzing a comprehensive dataset of mortgage applications and default outcomes to assess the interpretability and performance of these methodologies. The findings will provide valuable insights into the factors influencing mortgage default and aid in building transparent and trustworthy models in the lending industry.

Objective:

Investigate the use of Partial Dependence Plots (PDPs) to understand the impact of individual features on mortgage default predictions.
Evaluate the effectiveness of LIME (Local Interpretable Model-agnostic Explanations) in generating local explanations and identifying influential features for specific instances.
Explore the SHAP (SHapley Additive exPlanations) framework to quantify the global importance of features and identify key drivers of mortgage defaults.
Compare the interpretability, accuracy, and robustness of these methodologies for mortgage default prediction.
Provide insights and recommendations for utilizing these methodologies in the lending industry to enhance transparency and facilitate informed decision-making.
Methodology:

Data Collection: Gather a comprehensive dataset of mortgage applications, including borrower profiles, financial information, credit history, and default outcomes.

Data Preprocessing: Clean and preprocess the dataset, handle missing values, perform feature engineering, and split the data into training and testing sets.

Model Training: Build machine learning models for mortgage default prediction, including both black-box models (e.g., random forests) and interpretable models (e.g., decision trees).

PDP Analysis: Generate PDPs for selected features to understand their impact on default predictions while keeping other features fixed.

LIME Implementation: Apply LIME to obtain local explanations for specific instances, identifying influential features and their effect on individual predictions.

SHAP Analysis: Calculate SHAP values for all features, providing insights into their global importance and their interactions in predicting mortgage defaults.

Performance Evaluation: Assess the accuracy and robustness of the models using appropriate evaluation metrics and compare the interpretability of different methodologies.

Analysis and Conclusion: Analyze the findings, draw conclusions about the effectiveness and trade-offs of each methodology, and provide recommendations for using interpretability techniques in mortgage default prediction.

Expected Outcome:
The project will provide a comprehensive analysis of PDPs, LIME, and SHAP in the context of mortgage default prediction. The findings will contribute to the understanding of feature importance, model interpretability, and the factors driving mortgage defaults. The project's outcomes will assist lenders, regulators, and policymakers in making informed decisions and improving transparency in the lending industry.

Keywords: mortgage default prediction, interpretability, Partial Dependence Plots, LIME, SHAP, machine learning, feature importance, decision-making, transparency.