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Annotation of Chris Deotte's discussion about "How To Train XGBoost with Survival Loss".https://www.kaggle.com/competitions/equity-post-HCT-survival-predictions/discussion/550141 CIBMTR - Equity in post-HCT Survival PredictionsImprove prediction of transplant survival rates equitably for allogeneic HCT patientswww.kaggle.comHow To Train XGBoost with Survival LossThis competition involves trainin..

Annotation of Chris Deotte's discussion about "How To Get Started - Understanding the Metric".https://www.kaggle.com/competitions/equity-post-HCT-survival-predictions/discussion/550003 CIBMTR - Equity in post-HCT Survival PredictionsImprove prediction of transplant survival rates equitably for allogeneic HCT patientswww.kaggle.comC-Index ExplainedThe competition metric is Stratified Concordance ..

This is an annotation of this kernel:https://www.kaggle.com/code/cdeotte/gpu-lightgbm-baseline-cv-681-lb-685 GPU LightGBM Baseline - [CV 681 LB 685]Explore and run machine learning code with Kaggle Notebooks | Using data from CIBMTR - Equity in post-HCT Survival Predictionswww.kaggle.comGPU LightGBM BaselineIn this notebook, we present a GPU LightGBM baseline. In this notebook, compared to my pr..

Annotation of modeling & SHAP part of this kernel: Understanding Survival AnalysisExplore and run machine learning code with Kaggle Notebooks | Using data from CIBMTR - Equity in post-HCT Survival Predictionswww.kaggle.comXGBoost Model for SurvivalWe will now use an XGBoost model with Optuna to find the ideal hyperparameters. This model will be used to submit predictions.This XGBoost model imple..

Annotation of this kernel: https://www.kaggle.com/code/benjenkins96/understanding-survival-analysis Understanding Survival AnalysisExplore and run machine learning code with Kaggle Notebooks | Using data from CIBMTR - Equity in post-HCT Survival Predictionswww.kaggle.comInitial EDA# Check the distribution of the target variablesplt.figure(figsize=(10, 5))sns.countplot(data=train, x='efs', palett..

IntroductionBasically Survey Analysis CompetitionPredicting transplant survival rates for allogeneic HCT patientsallogeneic: transplanting cells, tissues, or organs from a donor of the same species who is not genetically identical to the recipientHCT: Hematopoietic Stem Cell Transplantation is a treatment method used to fundamentally treat diseases such as leukemia(백혈병) where abnormalities occur..

This is an annotation of code that produces datasets for YOLO solution with additional data(synthetic data)Trained weights: https://www.kaggle.com/datasets/sersasj/czii-yolo-l-trained-with-synthetic-data/dataCode: https://www.kaggle.com/code/sersasj/czii-making-datasets-for-yolo-synthetic-data#CZII:-Creating-Datasets-for-YOLO-with-Additional-DataCZII making datasets for YOLO + synthetic dataMod..

This post is an annotation of baseline YOLO11 solution kernel from @SERGIO ALVAREZ.https://www.kaggle.com/code/sersasj/czii-yolo11-submission-baseline-with-kdtree-update CZII YOLO11 Submission Baseline with KDTree UpdateExplore and run machine learning code with Kaggle Notebooks | Using data from multiple data sourceswww.kaggle.comCZII YOLO11 Submission Baseline with KDTree Update - LB 0.682Insp..

This post is an annotation of baseline unet solution kernel from "fnands".https://www.kaggle.com/code/ldywinner/baseline-unet-train-submit/notebook#Baseline-UNet-training-+-prediction/submission'Baseline UNet train + submit' - LB score 0.529Literally a baseline solution with no high lb scoreBased on 3 notebooks:https://www.kaggle.com/code/ahsuna123/3d-u-net-training-onlyhttps://www.kaggle.com/co..

This post is an annotation of training data code kernel from "fnands".https://www.kaggle.com/code/fnands/create-numpy-dataset-exp-name Create Numpy dataset exp nameExplore and run machine learning code with Kaggle Notebooks | Using data from CZII - CryoET Object Identificationwww.kaggle.comKernel 'Create Numpy dataset exp name'Overall this kernel is about PREPARING TRAINING DATA!pip install git+..