[b,~,~,inmodel,stats,~,~]=stepwisefit(traindata',trainVectorLabel,'penter',0.1,'premove',0.15,'maxiter',60,'display','off');
时间: 2024-01-19 09:02:05 浏览: 46
This code is using the stepwise feature selection method in MATLAB to select the best subset of features from the given training data.
The input arguments are:
- `traindata`: the training dataset, where each column represents a feature and each row represents an observation
- `trainVectorLabel`: the training labels for each observation
- `'penter'`: the threshold p-value for a feature to enter the model, set to 0.1
- `'premove'`: the threshold p-value for a feature to be removed from the model, set to 0.15
- `'maxiter'`: the maximum number of iterations for the algorithm to run, set to 60
- `'display'`: whether to display the stepwise selection process, set to `'off'`
The output variables are:
- `b`: the estimated coefficients for the selected features
- `inmodel`: a logical vector indicating which features are selected
- `stats`: a structure containing various statistics for the model
- the remaining output variables are not used in this code snippet