Can any Models be "Bagged"? I have been learning about "bagging" (bootstrap aggregation) - supposedly, there are many types of statistical models can be bagged together. For example, CART Decision Trees can be "bagged" together to from a Random Forest.
I am guessing that statistical models that are bagged together often lose some of their interpretability. For example, if you were to "bag" several regression models together, I am not sure if the regression coefficients of an ensemble of "bagged" regression models have the same interpretability and explanatory power as a single regression model.
This brings me to my question: Suppose some researchers are interested in fitting a Multi State Markov Model (e.g. https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf) to a very large dataset - suppose this dataset is so large (millions and millions of rows) that a Multi State Model can not be fit to the entire dataset. Thus, the researchers decide to take multiple random samples from this dataset, fit individual Multi State Models to each of these random samples - could these models then somehow be "bagged" together (I am not sure how transition probabilities from different Markov Chains can be aggregated together) and still keep their interpretability?  Or is "bagging" only used for very specific models and instances (e.g. Random Forest)?
Thanks!

Bagging is usually used for model so called the "weak learner" like Decision Tree. The reason behind is those weak learner usually overfit and lack of generalization power (High Variance).
With the application of Bagging , researcher found that it can reduce the variance of model (prevent overfitting). Althought some suggest that bagging would sometime reduce bias, mostly people use bagging to reduce overfitting. For underfitting, boosting may be a better idea.
For your case, your MSM model seems is underfitting the data instead of overfitting. Bagging might not be a good idea.
