You work as a machine learning specialist for an online real estate software company. Your company produces real estate listings with descriptions of properties, such as lot size, number of bedrooms, number of bathrooms, etc. You have been tasked with building a model to predict the value of the property. This value will be the estimated value displayed on the property listing. You are performing feature engineering of your data and you need to encode your categorical features to use in scikit-learn regression algorithms. You have dozens of categorical features, with many of the features having from 30 to 75 categories. Which encoding technique should you use for your categorical features?

Answer options:

A.One-hot-encoding
B.Label Encoding
C.Target Encoding with mean transform
D.Target Encoding with mean transform plus smoothing

Correct Answer: D
Option A is incorrect. One-hot-encoding with dozens of categorical features, some of which have 30 to 75 categories will explode your feature space.
Option B is incorrect. Since Label Encoding assigns a numerical value that is essentially an incremental count of the number of categories in the feature, it runs the risk of your regression algorithm assigning value to the order of the encoding.
Option C is incorrect. Target Encoding (sometimes referred to as Mean Encoding) with a mean transform works well but has issues with infrequent categories in your dataset, in other words a category that is found infrequently in your data source. Calculating a mean for a very small number of observations provides little differentiation.
Option D is correct. Combining Target Encoding using a mean transform with smoothing removes the disadvantages of Target Encoding by calculating the average of the category and the target together with the overall average.
Reference:
Please see the Towards Data Science article titled All about Categorical Variable Encoding (https://towardsdatascience.com/all-about-categorical-variable-encoding-305f3361fd02), and the Kaggle page titled Target Encoding (https://www.kaggle.com/ryanholbrook/target-encoding)