FAQ: Machine Learning Pipelines - Column Transformer

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This community-built FAQ covers the “Column Transformer” 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 Column Transformer

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This course has been launched while it is incomplete. For all the 8 pages of parctice code lesson, no code solution is provided so the learner cannot check the diff between their solution and code academy solution.

Page 8 - no solution - the CA solution is empty

Page 4 - col transformers

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3

it is still the same, please improve this course and its solutions.

2 Likes
4

cat_vals = Pipeline([(“imputer”,SimpleImputer(strategy=‘most_frequent’)), (“ohe”,OneHotEncoder(sparse=False, drop=‘first’))])
num_vals = Pipeline([(“imputer”,SimpleImputer(strategy=‘mean’)), (“scale”,StandardScaler())])

#Create the column transformer with the categorical and numerical processes
preprocess = ColumnTransformer(transformers=[
(“cat_process”, cat_vals, cat_cols),
(“num_process”, num_vals, num_cols)
])

#fit the preprocess transformer
preprocess.fit(x_train, y_train)

^^ That’s how I solved it.

1 Like
5

Thank you for sharing your solution! :slight_smile:

6

A few lessons in I there is still no explanation of why we are fitting the training data and transforming the test data. Why are we not transforming the training data??? Need to fit with x_train and y_train like commented by editbl76. Or just using x_train? I don’t think y_train is needed at this point since we are not jet fitting the models…but what do I know.

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