동선생

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+..

My goal is to be a ranker in Kaggle community and I want to be somekind of running mate with readers of my blog. So I am planning to write some posts like this that records how I am studying.  As of 2024.10.25, I have some background knowledge about data science because I took some undergraduate courses and studied for about 1 or 2 month before. So, I just started to compete in kaggle instead of..

해당 포스트는 auto encoder를 학습하던 중 지도 학습과 비지도 학습에 대한 개념을 학습하기 위해 작성되었습니다. 지도 학습(Supervised Learning)지도 학습이란 머신러닝 모델이 데이터를 학습할 때, 학습하는 데이터가 레이블(label)되어 있는 경우를 말합니다.  지도 학습의 종류에는 크게 회귀(Regression)와 분류(classification)이 있습니다. 회귀는 훈련 데이터와 이에 매칭되는 타겟 변수의 값들(레이블)을 학습하여, 새로운 입력 데이터에 대한 출력 값을 예측하는 기법입니다.분류는 말그대로 훈련 데이터와 이에 매칭되는 레이블을 학습하여, 새로운 입력 데이터의 범주형(categorical) 클래스를 예측하는 기법입니다.비지도 학습(Unsupervised Learni..