Project Background:
The project work for explainable feature engineering stems from increasingly deploying complex machine learning models in critical applications such as healthcare, finance, and legal domains where transparency and interpretability are paramount. As these sophisticated models often operate as “black boxes,” understanding the rationale behind their predictions becomes challenging. This given work module aims to address this challenge by delving into the realm of explainable feature engineering, a crucial aspect of model development that focuses on creating input variables that enhance the transparency of predictions.
This endeavor is motivated by the necessity to bridge the gap between the accuracy of predictive models and the need for stakeholders, including domain experts and end-users, to comprehend and trust the decision-making process. By laying a solid foundation in explainable feature engineering, it seeks to establish a framework for developing machine learning models that achieve high predictive performance and provide clear and interpretable insights into the factors influencing decisions by promoting trust, accountability, and ethical considerations in various real-world applications.
Problem Statement
- As organizations increasingly rely on sophisticated models for decision-making, the lack of transparency poses challenges in understanding how and why specific predictions are made.
- The crux of the problem lies in the need to balance model accuracy with interpretability.
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Complex models, often highly predictive, lack transparency, making it difficult for stakeholders, including domain experts and end-users, to trust and comprehend the decision-making process.
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The challenge is to systematically create and select input variables that contribute to accurate predictions and enhance the overall models interpretability.
Aim and Objectives
- The project aims to enhance transparency and interpretability through effective explainable feature engineering, particularly in critical domains like healthcare, finance, and legal systems.
- Develop a systematic framework for creating interpretable features that contribute to accurate predictions.
- Investigate and implement automated tools to streamline the process of explainable feature engineering.
- Collaborate with domain experts to ensure the selected features align with real-world insights and requirements.
- Evaluate the impact of model transparency and trustworthiness.
- Explore methods for dynamically adjusting the level of explainability based on user needs or contextual factors.
- Address ethical considerations in feature engineering to prevent the propagation of biases and ensure fairness.
- Provide actionable insights to stakeholders by visualizing the relationships between features and model predictions.
- Assess the scalability of explainable feature engineering techniques for large datasets and complex models.
Contributions to Explainable Feature Engineering
- The project contributes by developing a systematic framework for explainable feature engineering, providing a structured approach to creating interpretable input variables.
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Assessing and quantifying the impact on model transparency and trustworthiness of incorporating interpretability into the model-building process.
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Introducing automated tools that streamline the process of reducing the manual effort required and facilitating the efficient creation of transparent models.
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Introducing visualization techniques offers stakeholders actionable insights into the relationships between features and model predictions, enhancing the interpretability of the entire model.
- Investigating methods for dynamically adjusting the level of explainability, catering to user needs or contextual factors, and providing flexibility in tailoring the transparency of models.
- Contributing to ethical considerations prevents biases and promotes fairness, ensuring the developed models adhere to ethical standards in critical decision-making processes.
Deep Learning Algorithms for Explainable Feature Engineering
- LIME
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SHAP
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Layer-wise Relevance Propagation (LRP)
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DeepLIFT
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Grad-CAM
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Integrated Gradients
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Saliency Maps
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Layer-wise Relevance Propagation
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Decision Trees based on Neural Networks
Datasets for Explainable Feature Engineering
- UCI Machine Learning Repository
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Kaggle Datasets for Explainability
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FICO Explainable Machine Learning Challenge Dataset
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Adult Income dataset
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MNIST for image classification explainability
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IMDB Movie Reviews for natural language processing explainability
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PhysioNet Challenge datasets for healthcare applications
Performance Metrics for Explainable Feature Engineering
- Accuracy
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Precision
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Recall
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F1 Score
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Area Under the Receiver Operating Characteristic (ROC-AUC)
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Mean Squared Error (MSE)
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R-squared (R2)
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Cohens Kappa
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
