Take suggestions into account

Author: mfeurer

doc/faq.rst

@@ -42,7 +42,7 @@ General
       dtype for your task. By default *auto-sklearn* treats object and string columns as strings and
       encodes the data using `sklearn.feature_extraction.text.CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
     * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical.
-      data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_
+      Data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_
       for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html>`_ for multidimensional data.
     * For further details on how to properly encode your data, you can check the Pandas Example
       `Working with categorical data <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`_). If you are working with time series, it is recommended that you follow this approach

doc/manual.rst

@@ -328,7 +328,7 @@ Other
       dtype for your task. By default *auto-sklearn* treats object and string columns as strings and
       encodes the data using `sklearn.feature_extraction.text.CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
     * If your data contains categorical values (in the features or targets), ensure that you explicitly label them as categorical.
-      data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_
+      Data labeled as categorical is encoded by using a `sklearn.preprocessing.LabelEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_
       for unidimensional data and a `sklearn.preprodcessing.OrdinalEncoder <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html>`_ for multidimensional data.
     * For further details on how to properly encode your data, you can check the Pandas Example
       `Working with categorical data <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`_). If you are working with time series, it is recommended that you follow this approach