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Exercise

Kidney disease case study I: Categorical Imputer

You'll now continue your exploration of using pipelines with a dataset that requires significantly more wrangling. The chronic kidney disease dataset contains both categorical and numeric features, but contains lots of missing values. The goal here is to predict who has chronic kidney disease given various blood indicators as features.

As Sergey mentioned in the video, you'll be introduced to a new library, sklearn_pandas, that allows you to chain many more processing steps inside of a pipeline than are currently supported in scikit-learn. Specifically, you'll be able to impute missing categorical values directly using the Categorical_Imputer() class in sklearn_pandas, and the DataFrameMapper() class to apply any arbitrary sklearn-compatible transformer on DataFrame columns, where the resulting output can be either a NumPy array or DataFrame.

We've also created a transformer called a Dictifier that encapsulates converting a DataFrame using .to_dict("records") without you having to do it explicitly (and so that it works in a pipeline). Finally, we've also provided the list of feature names in kidney_feature_names, the target name in kidney_target_name, the features in X, and the target in y.

In this exercise, your task is to apply the CategoricalImputer to impute all of the categorical columns in the dataset. You can refer to how the numeric imputation mapper was created as a template. Notice the keyword arguments input_df=True and df_out=True? This is so that you can work with DataFrames instead of arrays. By default, the transformers are passed a numpy array of the selected columns as input, and as a result, the output of the DataFrame mapper is also an array. Scikit-learn transformers have historically been designed to work with numpy arrays, not pandas DataFrames, even though their basic indexing interfaces are similar.

Instructions
100 XP
  • Apply the categorical imputer using DataFrameMapper() and CategoricalImputer(). CategoricalImputer() does not need any arguments to be passed in. The columns are contained in categorical_columns. Be sure to specify input_df=True and df_out=True, and use category_feature as your iterator variable in the list comprehension.
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