FAQ: Machine Learning Pipelines - Writing Custom Classes & Summary

This community-built FAQ covers the “Writing Custom Classes & Summary” exercise from the lesson “Machine Learning Pipelines”.

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Machine Learning/AI Engineer Career Path

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please add the solution of this task

Since the solution is still missing, here is mine in case anyone needs it:

#1. Create new pipeline using the custom class MyImputer as the first step and standard scaler on the second

#Create the new Pipeline instance
new_pipeline = Pipeline([
(‘imputer’, MyImputer()), #first step is to instantiate a MyImputer object with fit and transform methods to replicate SimpleImputer with ‘mean’ strategy
(‘scaler’, StandardScaler()) #second step is to add the Scaler object
])

#2. Fit new pipeline on the training data with num_cols only and verify that the results of the transform are the same on test set

#Fit the numerical data from the training set
new_pipeline.fit(x_train[num_cols])
print(‘Verify pipeline transform test set is the same\nPrinting the sum of absolute differences:’)
#Get the sum of absolute difference between the two datasets to verify the result
print(abs(new_pipeline.transform(x_test[num_cols]) - x_test_fill_missing_scale).sum())