RandomForest surprisingly high accuracy
You can be sure that saving the model to disk won't make the model have higher accuracy when loaded again. Saving the models to disk would only cause worse performance if parameters are truncated, for example. 

To establish a fair and rigorous comparison method, you have to keep track of your training/test splitting result. In that sense, you can:


Make your code reproducible, using random seeds conveniently 
Before saving the model, you can save your splitting results by either saving the data or returning the indices for the training set and the test set. For that purpose you have to store the data-set in a list (for example, a list with the file names…)