Redefining-Cancer-Treatment-Multi-Class-Log-Loss

Multi Class Log Loss between the predicted probability and the observed target. Personalized Medicine: Redefining Cancer Treatment A kaggle data exploration About the data set: Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). Currently this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature. For this competition MSKCC is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations. We need your help to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations. the kernel on kaggle: https://www.kaggle.com/sousablde/breast-cancer-mutation-effect-on-oncogenecity/edit/run/18611091