Cross-validation Method for Tuning Parameter Interval Selection
The existing cross-validation methods generally select an optimal tuning parameter from the given parameter value when se-lecting the tuning parameters of the model,and in order to cope with the complexity of the values and improve the convenience of se-lection,it is often preferred to select a parameter interval in practical applications,which is also conducive to observing the stability of the algorithm.To solve this problem,this paper improves the block-regularized m x 2cross-validation method,and proposes a new model adjustment parameter interval selection method,the basic idea is to give multiple tuning parameter intervals,using incremental methods,constantly increasing m,and then continuously reducing the number of tuning parameter intervals.Finally,an optimal tuning parameter interval is selected,and any tuning parameters are selected in this optimal interval,which can be used as the tuning parame-ters of the model.Through a large number of experiments,compared with tuning parameter selection methods based on cross-validation model(block-regularized m x2cross-validation method,2-fold,5-fold,10-fold cross-validation),the average accuracy of the model in the selected interval is not much different from the accuracy of the optimal single parameter,and the difference between the highest ac-curacy and the lowest accuracy in the interval is very small,indicating that the performance of selecting parameters as tuning parame-ters in this interval is relatively stable.
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