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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).
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).
This is a SteamLit Web-App which delves in Exploratory Data Analysis with Iris, Breast-Cancer and Wine datasets using ML models like KNN's, SVM's and Random Forests
[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.
Open Source Breast Cancer Research for Summer Research Program ASIME 2024 @ Adelphi University, New York. Data from the UV Irvine Machine Learning Repository.