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breast-cancer-dataset

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In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

  • Updated Jul 6, 2023
  • Jupyter Notebook

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

  • Updated Jul 9, 2022
  • Jupyter Notebook
Machine-Learning-Using-Python

[Big Data Analytics] This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection.

  • Updated Nov 22, 2021
  • Jupyter Notebook

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