https://rfriend.tistory.com/188
[R 기계학습] 과적합(Over-fitting)을 피할 수 있는 방법은? (Training vs. Validation vs. Test set)
지난번 포스팅에서는 과적합(Over-fitting)이란 무엇인지, 그리고 과적합이 왜 문제인지에, 과적합이 아니라 데이터에 내재한 구조, 관계, 규칙을 일반화(generalization)하여 적정적합을 시킬 수 있도록 학습하는..
rfriend.tistory.com
K-Fold Cross Validation(교차검증) 정의 및 설명
정의 - K개의 fold를 만들어서 진행하는 교차검증 사용 이유 - 총 데이터 갯수가 적은 데이터 셋에 대하여 정확도를 향상시킬수 있음 - 이는 기존에 Training / Validation / Test 세 개의 집단으로 분류하는 것..
nonmeyet.tistory.com
https://3months.tistory.com/118
Machine Learning에서 validation set을 사용하는 이유
validation set은 machine learning 또는 통계에서 기본적인 개념 중 하나입니다. 하지만 실무를 할때 귀찮은 부분 중 하나이며 간과되기도 합니다. 그냥 training set으로 training을 하고 test만 하면 되지 왜..
3months.tistory.com
https://en.wikipedia.org/wiki/Cross-validation_(statistics)
Cross-validation (statistics) - Wikipedia
Diagram of k-fold cross-validation. Cross-validation, sometimes called rotation estimation[1][2][3] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to a
en.wikipedia.org
https://machinelearningmastery.com/k-fold-cross-validation/
A Gentle Introduction to k-fold Cross-Validation
Cross-validation is a statistical method used to estimate the skill of machine learning models. It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to
machinelearningmastery.com
https://scikit-learn.org/stable/modules/cross_validation.html
3.1. Cross-validation: evaluating estimator performance — scikit-learn 0.22.1 documentation
3.1. Cross-validation: evaluating estimator performance Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have
scikit-learn.org
https://discuss.pytorch.org/t/what-is-the-proper-processing-applying-kfold/37444
What is the proper processing applying kfold?
I am confused about how to evaluate in stratified kfold CV. According to the documentation, performance is evaluated the average, but I do not know what the average means. So far, I split the fold using stratified kfold CV, and training and validation for
discuss.pytorch.org