Selçuk Korkmaz (@selcukorkmaz) 's Twitter Profile
Selçuk Korkmaz

@selcukorkmaz

Biostatistics PhD | R package dev | AI in medicine | Dad of 2 | Deputy editor @balkanmedj (SCIE, Q2)

ID: 284531684

linkhttps://scholar.google.com/citations?user=TKOcnUwAAAAJ&hl=ao calendar_today19-04-2011 13:17:44

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18,18K Followers

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Selçuk Korkmaz (@selcukorkmaz) 's Twitter Profile Photo

Say goodbye to cumbersome machine learning workflows in R! Meet fastml – your new go-to package for streamlined training, evaluation, and comparison of multiple ML models with minimal code. #rstats #MachineLearning #DataScience github.com/selcukorkmaz/f…

Selçuk Korkmaz (@selcukorkmaz) 's Twitter Profile Photo

fastml Tutorial: From Simple to Advanced This tutorial demonstrates how to use fastml through a series of progressively complex examples. Each example introduces new arguments and techniques. #Rstats #DataScience #MachineLearning github.com/selcukorkmaz/f…

Selçuk Korkmaz (@selcukorkmaz) 's Twitter Profile Photo

🇹🇷 A historic moment for Turkish medicine! 🎉 Balkan Medical Journal (Balkan Medical Journal) becomes the first medical journal from Türkiye ranked in Q1 across all medical fields (Web of Science 2024)! 📈 Impact Factor: 3.8 🏅 Rank: 50/332 👏 Congratulations to the entire team!

🇹🇷 A historic moment for Turkish medicine!
🎉 Balkan Medical Journal (<a href="/balkanmedj/">Balkan Medical Journal</a>) becomes the first medical journal from Türkiye ranked in Q1 across all medical fields (Web of Science 2024)!
📈 Impact Factor: 3.8
🏅 Rank: 50/332
👏 Congratulations to the entire team!
Selçuk Korkmaz (@selcukorkmaz) 's Twitter Profile Photo

Just used fastml to compare logistic regression (glm/glmnet) and random forest (ranger/randomForest) on the Framingham dataset. Repeated CV + Bayesian tuning (20 iter) with early stopping, MICE imputation, and upsampling made model selection easy. 🚀 library(fastml)

Just used fastml to compare logistic regression (glm/glmnet) and random forest (ranger/randomForest) on the Framingham dataset. Repeated CV + Bayesian tuning (20 iter) with early stopping, MICE imputation, and upsampling made model selection easy. 🚀

library(fastml)