One-Hot encoding
The problem with label encoding is that it implicitly assumes that there is a ranking dependency between the categories. So, let's change the encoding method for the features "RoofStyle" and "CentralAir" to one-hot encoding. Again, the train and test DataFrames from House Prices Kaggle competition are already available in your workspace.
Recall that if you're dealing with binary features (categorical features with only two categories) it is suggested to apply label encoder only.
Your goal is to determine which of the mentioned features is not binary, and to apply one-hot encoding only to this one.
Questo esercizio fa parte del corso
Winning a Kaggle Competition in Python
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Concatenate train and test together
houses = pd.concat([train, test])
# Look at feature distributions
print(houses['RoofStyle'].____, '\n')
print(houses['CentralAir'].____)