Multiple predicting K-fold cross-validation for model selection

By Yoonsuh Jung. 2018

463 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/57886953efceaff48b660f1005907ae8c73f1a04

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Guoyan Zhao
2. Weizhang Liang
3. Hao Wu
4. Suizhi Luo
5. Qiang Ning
6. Nanyun Peng
7. Rujun Han

In Molecular Biology

1. Guoyan Zhao
2. Weizhang Liang
3. Hao Wu
4. Suizhi Luo

In Statistics and Probability

1. Guoyan Zhao
2. Weizhang Liang
3. Hao Wu
4. Suizhi Luo

Citations in the corpus

• Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms (2020, methodology): “Assume the confusion matrix is expressed as where E is the number of pillar stability levels, g aa represents the number of samples correctly predicted for level a , and g ab denotes the number of samples of level a that are classified to level b .”