How is Cross Validation Implemented in Catalysis Studies?
In catalysis, cross validation can be implemented using several methods: - K-fold cross validation: The dataset is divided into k equal-sized folds. The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, and the results are averaged. - Leave-one-out cross validation (LOOCV): Each observation is used as a single validation point while the rest of the dataset is used for training. This is repeated for each observation. - Random subsampling: Multiple random splits of the dataset are created, and the model is trained and validated on these splits.
