Normally, the first layer models in stacking and bledning method are trained by all features in a data set. However, what will be the performance of a big model based on first layer models trained by different features in a same data set?
For example, if a big model contains 4 first layer models was built to predict the labels of a data set which contains 12 features. Now, instead of using all 12 features to train each of the first layer models, the data set is divided into 4 subsets which each of them contains partial features. For each of the subsets, it is used to train one of the sub models. What will be the accuracy of this model comparing to the model using all 12 featues to train each of its first layer models?