Linear Regression vs ML Sports Analytics Students Outsmart Betting

84% of betting odds missed the mark after simple data cleaning. A team of sports analytics students proved a well‑tuned linear model can outplay flashy AI. Find out how they did it and what it means for future bettors.

https://sportsanalyticsnow.online/linear-regression-vs-ml-sports-analytics-students/

#sportsanalyticsstudents #superbowllxprediction #regressionmodel #crossvalidation #machinelearningcompetition

Linear Regression vs ML Sports Analytics Students Outsmart Betting

Explore how sports analytics students leveraged linear regression and meticulous data cleaning to beat betting odds, surpassing popular machine‑learning models in a

Sports Analytics Now

Marginal likelihood is exhaustive leave-p-out cross-validation

이 글은 로그 주변우도(log marginal likelihood, LML)가 모든 가능한 학습-검증 분할에 대한 평균을 취한 완전한 leave-p-out 교차검증과 동일하다는 점을 수학적으로 증명한다. LML은 베이지안 모델 선택에서 중요한 역할을 하지만, 일반화 성능을 완벽히 대변하지는 못하며, 특히 적은 데이터에 조건부인 경우가 많아 한계가 있다. 다만, 가우시안 프로세스 같은 특정 모델에서는 LML을 효율적으로 계산할 수 있어 실용적이다. 이 연구는 베이지안 모델 선택과 전통적 교차검증 간의 연결고리를 명확히 한다.

https://belko.xyz/posts/lml-and-cross-validation/

#bayesian #marginallikelihood #crossvalidation #modelselection #gaussianprocesses

Marginal likelihood is exhaustive leave-p-out cross-validation

Release Drugresponseeval 1.1.0 - Humongous Zapdos · nf-core/drugresponseeval

What's Changed Added #43 Preprint is out now! Linking it in the documentation. #42 Added authors and licenses to the python scripts. #43 Added --no_hyperparameter_tuning flag for quick runs withou...

GitHub

Кросс-валидация на временных рядах: как не перемешать время

Привет, Хабр! Сегодня рассмотрим то, что чаще всего ломает даже круто выглядящие модели при работе с временными рядами — неправильная кросс‑валидация . Разберем, почему KFold тут не работает, как легко словить утечку будущего, какие сплиттеры реально честны по отношению ко времени, как валидировать фичи с лагами и агрегатами.

https://habr.com/ru/companies/otus/articles/921604/

#временные_ряды #time_series #машинное_обучение #прогнозирование #кроссвалидация #crossvalidation

Кросс-валидация на временных рядах: как не перемешать время

Привет, Хабр! Сегодня рассмотрим то, что чаще всего ломает даже круто выглядящие модели при работе с временными рядами — неправильная кросс-валидация . Разберем, почему KFold тут не работает, как...

Хабр

Cross-Validation là gì trong Machine Learning? A-Z

Cross-Validation là một kỹ thuật then chốt trong Machine Learning, giúp kiểm tra hiệu suất và khả năng tổng quát của mô hình trên dữ liệu mới. Nhờ đó, mô hình tránh được tình trạng học lệch và hoạt động ổn định hơn. Bài viết này sẽ cùng bạn khám phá chi tiết về Cross-Validation, lý do nó quan trọng và các phương pháp xác thực phổ biến hiện nay.

Xem chi tiết bài viết tại đây: https://interdata.vn/blog/cross-validation-la-gi/

#interdata #crossvalidation

Guide to Cross-Validation in Machine Learning - NeuralRow - Medium

The basic idea of cross-validation is to split the data first into training and test parts. Then, the training part is further divided into subtrain and validation parts, cycling through these…

Medium
Release 1.0.0 · nf-core/drugresponseeval

What's Changed Important! Template update for nf-core/tools v3.0.1 by @nf-core-bot in #10 Merge branch 'dev' of github.com:nf-core/drugresponseeval into dev by @JudithBernett in #11 Global checkpo...

GitHub

#statstab #103 On the marginal likelihood and cross-validation

Thoughts: Can't say I can follow much of this, so I'll open it up to the #bayesian community for input. Seems important though.

#stats #bayes #likelihood #evidence #crossvalidation

https://doi.org/10.1093/biomet/asz077

On the marginal likelihood and cross-validation

Summary. In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability

OUP Academic

⬆️

6) thankfully, Wager (2020) https://doi.org/10.1080/01621459.2020.1727235 shows that cross-validation is asymptotically consistant for model selection, so while what we're doing gives us poor estimates of generalization error and bad error bars, at least it's valid for model selection.

#machineLearning #statistics #crossValidation