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3647998

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is In Organization
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is In Organization
Yes
description
Of all the applications of artificial intelligence, diagnosing any disease using a "black box" is always going to be a hard explanation. Those who will use the application will want to know how the model decides on the treatment conditions or following-up conditions according to the model result. Or data provider clinicians will want the model with the highest performance in their project. This dataset classified patients according to sacral position properties. I investigated using the below techniques for the best result and explainable machine learning model; Balancing unbalanced medical data Creating models with CatBoost Classifier Finding the most optimized parameters by Grid Search with the Optuna library Artificial intelligence algorithms described as Black Box are actually explainable SHAP library tutorial Combined use of RFECV and SHAP library for Feature Selection Comparison of all applied models to each other
description
Laravel is a web application framework with expressive, elegant syntax. We’ve already laid the foundation for your next big idea — freeing you to create without sweating the small things.
homepage
homepage
https://laravel.com
url
https://githubhelp/turkalpmd/catboost_shap_smote
url
https://githubhelp/laravel/laravel
disk Usage
1640
dis kUsage
10604
stargazer Count
1
stargazer Count
77580
forkCount
0
forkCount
23942
watcher Count
1
watcher Count
77580
created At
2022-07-27
created At
2011-06-08
license Info
Apache License 2.0
license Info
issues
0
issues
0
languages
1
languages
3
repository Topics
0
repository Topics
3
owner
turkalpmd
owner
laravel
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owner Avatar
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other
other

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