Encoding categorical columns II: OneHotEncoder

Okay - so you have your categorical columns encoded numerically. Can you now move onto using pipelines and XGBoost? Not yet! In the categorical columns of this dataset, there is no natural ordering between the entries. As an example: Using LabelEncoder, the CollgCr Neighborhood was encoded as 5, while the Veenker Neighborhood was encoded as 24, and Crawfor as 6. Is Veenker "greater" than Crawfor and CollgCr? No - and allowing the model to assume this natural ordering may result in poor performance.

As a result, there is another step needed: You have to apply a one-hot encoding to create binary, or "dummy" variables. You can do this using scikit-learn's OneHotEncoder.

Import OneHotEncoder from sklearn.preprocessing.
Instantiate a OneHotEncoder object called ohe. Specify the keyword arguments categorical_features=categorical_mask and sparse=False.
Using its .fit_transform() method, apply the OneHotEncoder to df and save the result as df_encoded. The output will be a NumPy array.
Print the first 5 rows of df_encoded, and then the shape of df as well as df_encoded to compare the difference.

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Encoding categorical columns II: OneHotEncoder

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