Breast Cancer Diagnosis Prediction Using LIME Model

[inkerton]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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Issue Title

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Explainable AI on Cancer Dataset

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Info about the Related Issue

What's the goal of the project?
The main objective of this project is to predict whether a tumor is benign or malignant using machine learning models and explain the model’s decisions using LIME (Local Interpretable Model-Agnostic Explanations). The dataset used in this project is the Breast Cancer Dataset available from Kaggle.

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Name

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Janvi

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GitHub ID

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inkerton

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Email ID

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janvichoudhary116@gmail.com

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Identify Yourself

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GSSOC

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Closes

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Closes: #issue-number #302

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Describe the Add-ons or Changes You've Made

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• Decision Tree Classifier: A tree-based model used for classification of tumors based on their features.

• LIME (Local Interpretable Model-Agnostic Explanations): Used to explain predictions of the machine learning model by generating interpretable explanations for individual instances.

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Type of Change

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• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

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How Has This Been Tested?

Describe how your changes have been tested.

1. Unit Testing: Each individual function and module has been tested to ensure they perform as expected. For example, data preprocessing steps and model predictions were verified with known inputs.

2. Model Evaluation:

   • The model was trained and validated using cross-validation techniques to assess its performance metrics, such as accuracy, precision, recall, and F1-score.
   • Confusion matrices were used to visualize and analyze classification results.

3. LIME Interpretability:

   • The LIME (Local Interpretable Model-agnostic Explanations) method was applied to individual predictions, validating that the model’s decisions were interpretable and consistent with domain knowledge.

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Checklist

Please confirm the following:

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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[inkerton]

@UTSAVS26 Any update on my PR acceptance.