text: Title: CleanLearning default classifier
  Body: Currently CleanLearning can be run like this which is nice for users who don't know what classifier to use for their data:
  ```
  cl = CleanLearning()
  cl.find_label_issues(data, labels)
  ```
  but it always defaults to sklearn's LogisticRegression, which may not work for many types of `data`. Consider deferring the choice of default classifier until `data` is provided and then selecting from a broader suite of default options beyond LogisticRegression as well to make this work for more data types.  
  
  Challenge is how to do this without introducing additional dependencies for the cleanlab package, or making this code too complex.  This challenge makes this quite complex without developing a full autoML strategy, which we don't want to do here.  
  
  A useful contribution in the meantime could be just to provide better error handling when the default LogisticRegression classifier won't work (eg. the dataset is a pytorch or tensorflow dataset).
  