Merge pull request #102186 from Aniththa/patch-37

Update how-to-configure-auto-train.md

Author: PRMerger15

articles/machine-learning/how-to-configure-auto-train.md

@@ -182,11 +182,11 @@ The primary metric determines the metric to be used during model training for op
 
 Learn about the specific definitions of these metrics in [Understand automated machine learning results](how-to-understand-automated-ml.md).
 
-### Data preprocessing & featurization
+### Data featurization
 
-In every automated machine learning experiment, your data is [automatically scaled and normalized](concept-automated-ml.md#preprocess) to help *certain* algorithms that are sensitive to features that are on different scales.  However, you can also enable additional preprocessing/featurization, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-create-portal-experiments.md#preprocess).
+In every automated machine learning experiment, your data is [automatically scaled and normalized](concept-automated-ml.md#preprocess) to help *certain* algorithms that are sensitive to features that are on different scales.  However, you can also enable additional featurization, such as missing values imputation, encoding, and transforms. [Learn more about what featurization is included](how-to-create-portal-experiments.md#preprocess).
 
-To enable this featurization, specify `"preprocess": True` for the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
+To enable this featurization, specify `"featurization": 'auto'` for the [`AutoMLConfig` class](https://docs.microsoft.com/python/api/azureml-train-automl/azureml.train.automl.automlconfig?view=azure-ml-py).
 
 > [!NOTE]
 > Automated machine learning pre-processing steps (feature normalization, handling missing data,
@@ -322,7 +322,7 @@ You can view your training results in a widget or inline if you are in a noteboo
 ## Understand automated ML models
 
 Any model produced using automated ML includes the following steps:
-+ Automated feature engineering (if preprocess=True)
++ Automated feature engineering (if `"featurization": 'auto'`)
 + Scaling/Normalization and algorithm with hyperparameter values
 
 We make it transparent to get this information from the fitted_model output from automated ML.
@@ -335,7 +335,7 @@ best_run, fitted_model = automl_run.get_output()
 
 ### Automated feature engineering
 
-See the list of preprocessing and [automated feature engineering](concept-automated-ml.md#preprocess) that happens when feauturization =auto.
+See the list of preprocessing and [automated feature engineering](concept-automated-ml.md#preprocess) that happens when `"featurization": 'auto'`.
 
 Consider this example:
 + There are four input features: A (Numeric), B (Numeric), C (Numeric), D (DateTime)