Use ``TrainRegressor`` to train a regression model on a dataset.

Below is an example that uses ``TrainRegressor``.  Given a DataFrame,
myDataFrame, with a label column, "MyLabel", split the DataFrame into
train and test sets.  Train a regressor on the dataset with a solver,
such as l-bfgs:

>>> from synapse.ml.TrainRegressor import TrainRegressor
>>> from pysppark.ml.regression import LinearRegression
>>> lr = LinearRegression().setSolver("l-bfgs").setRegParam(0.1).setElasticNetParam(0.3)
>>> model = TrainRegressor(model=lr, labelCol="MyLabel", numFeatures=1 << 18).fit(train)

Now that you have a model, you can score the regressor on the test data:

>>> scoredData = model.transform(test)
