neil-ab/classification-xgboost-bayesopt
A jupyter notebook for binary classification of breast cancer using XGBoost with Bayesian optimization.
This project helps medical professionals or researchers analyze breast cancer indicators to predict whether a tumor is benign or malignant. It takes a dataset of various numeric measurements related to breast cancer and outputs a trained model that can classify new cases with an accuracy score and visualizations like feature importance. This is designed for someone who needs to build a predictive model for medical diagnosis.
No commits in the last 6 months.
Use this if you need to quickly build and optimize a classification model for breast cancer diagnosis using a robust machine learning approach.
Not ideal if you are looking for a general-purpose, re-usable medical diagnostic system rather than a specific analysis of a given dataset.
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9
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Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Apr 19, 2022
Commits (30d)
0
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