Continuing to fix more unit tests.

-  Updated `test_specified_cv_folds`
   - Modified `make_cv_folds_data()` to use `make_classification` and generate a `FeatureSet` directly.
   - Modified `test_specified_cv_folds()` to run the tests without using config files and with and without feature hashing both.
-  Updated `test_sparse_predict`
   - Removed a few other functions that were no longer required.
   - Modified `make_sparse_data` to use `make_classification` from scikit-learn and return `FeatureSets`.
   - Modified `test_sparse_predict` to use a check function that can run tests with and without feature hashing.
-  Updated `test_scaling`
   - Create `FeatureSet` objects directly
   - Make tests deterministic with both feature hashing and scaling
-  Made sure checking large feature values in `learner.py` happens after scaling
   - Split `_check_input()` into two separate functions: `_check_input_formatting()` and `_check_max_feature_value()`. The first is called where `_check_input()` was originally called and the second is called after scaling happens inside `train()`.
- Fixed bug with default value of `train_test_ratio`
- Used `NDJWriter` instead of deprecated `write_feature_file` where we were specifically writing `.jsonlines` files.
- Collapsed all `specified_cv_folds` tests into one function
    - Modified `make_cv_folds_data` to use fewer examples otherwise the test takes too long when using `RBFSampler`.
    - Collapsed all `specified_cv_folds` tests with/without feature-hashing and with/without sampling into a single function.