You are a machine learning consultant who has been contracted to evaluate and correct a model built by a client’s machine learning team. The team’s model performs poorly against their selected metric when the team tries to fit the model. The model is built using scikit-learn and uses the RandomForest algorithm. After doing some investigation you see that the data source has missing values in both numeric and categorical features and the machine learning team chose the strategy of dropping the features with missing data. You want to use the scikit-learn ColumnTransformer class to transform the missing data, specifically replacing the missing data in the numeric and categorical data features using imputation. You have decided to replace the numeric missing values with predictions for the missing values, and the categorical missing values with the most frequent value in the feature. Which of the following are the best approaches to achieving your goal? (Select TWO)

Answer options:

A.Create a one-step preprocessing transformer for the numerical missing values that uses a SimpleImputer using an ExtraTreesRegressor estimator
B.Create a one-step preprocessing transformer for the numerical missing values thatuses a SimpleImputer using the constant strategy
C.Create a two-step preprocessing transformer for the categorical missing values that uses a SimpleImputer using the most_frequent strategy then uses the OneHotEncoder in step two to encode the categorical data
D.Create a two-step preprocessing transformer for the categorical missing values that uses an IterativeImputer using the most_frequent strategy then uses the OneHotEncoder in step two to encode the categorical data
E.Create a one-step preprocessing transformer for the numerical missing values thatuses a KNNImputer

Correct Answers: C and E
Option A is incorrect. The scikit-learn SimpleImputer does not have an ExtraTreesRegressor estimator.
Option B is incorrect. This approach will replace all of your missing numerical values with 0, the default for the constant strategy. This will not achieve your goal of replacing the missing numeric values with predictions for the missing values.
Option C is correct. Using the scikit-learn SimpleImputer with the most_frequent strategy and then using the OneHotEncoder class will get the results you are trying to achieve.
Option D is incorrect. You could use the IterativeImputer class with the most_frequent strategy, but the IterativeImputer class is released as experimental. The SimpleImputer class using the most_frequent strategy is a better choice.
Option E is correct. Using the scikit-learn KNNImputer class to impute your numeric missing values will give you the results you need of replacing the missing values with estimates.
Reference:
Please see the Scikit-learn modules page titled sklearn.impute.SimpleImputer (https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html), the Scikit-learn modules page titled sklearn.impute.IterativeImputer (https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer), the Scikit-learn modules page titled sklearn.impute.KNNImputer (https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html#sklearn.impute.KNNImputer), the Scikit-learn modules page titled 6.4. Imputation of missing values (https://scikit-learn.org/stable/modules/impute.html), and the Kaggle page titled Pipelines (https://www.kaggle.com/alexisbcook/pipelines)