FAQ: Machine Learning Pipelines - Data Cleaning (Categorical)

This community-built FAQ covers the “Data Cleaning (Categorical)” exercise from the lesson “Machine Learning Pipelines”.

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This exercise can be found in the following Codecademy content:

Machine Learning/AI Engineer Career Path

FAQs on the exercise Data Cleaning (Categorical)

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This exercise is poorly written. It says that you should use sparse=‘False’, but sparse parameter does not take a string argument, but a bool. So it should be sparse=False.
Also, the exercise asks you to compare x_test_fill_missing_ohe with the transformation you get when calling the pipeline on x_test_fill_missing. However, it does not mention that the one hot encoder used to create x_test_fill_missing_ohe has a second parameter which is drop=‘first’. This lack of information causes confusion because when you try to compute the absolute difference between the 2 matrices, you get the error:

ValueError: operands could not be broadcast together with shapes (1045,2) (1045,3)