Custom transformers in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom transformer in machine learning?

A custom transformer is a user-made tool that changes data in a specific way before using it in a model. It helps prepare or clean data to make the model work better.

beginner

Which two methods must a custom transformer implement in scikit-learn?

A custom transformer must implement fit() to learn from data and transform() to change the data based on what it learned.

intermediate

Why use custom transformers instead of built-in ones?

Custom transformers let you handle special data or do unique changes that built-in transformers can't do. This makes your model better for your specific problem.

intermediate

How does a custom transformer fit into a machine learning pipeline?

It acts as a step that changes data before the model sees it. This keeps data preparation organized and repeatable for training and testing.

beginner

What is the purpose of the fit_transform() method in custom transformers?

It combines fit() and transform() in one step to learn from data and then immediately change it, saving time and code.

What method in a custom transformer changes the data?

A fit()

B transform()

C predict()

D score()

The transform() method changes the data based on what was learned in fit().

Why might you create a custom transformer?

A To visualize data

B To speed up model training by skipping data preparation

C To handle special data transformations not covered by built-in tools

D To replace the model's prediction step

Custom transformers are made to do special data changes that built-in transformers can't do.

Which scikit-learn class is commonly extended to create a custom transformer?

A ClassifierMixin

B Pipeline

C RegressorMixin

D BaseEstimator and TransformerMixin

Extending BaseEstimator and TransformerMixin helps create custom transformers with standard methods.

What does the fit() method do in a custom transformer?

A Learns parameters from the data

B Changes the data

C Makes predictions

D Evaluates model accuracy

fit() learns from the data to prepare for transformation.

Where in a machine learning workflow is a custom transformer used?

A Before the model to prepare data

B After the model to check results

C During prediction to improve speed

D To replace the model training

Custom transformers prepare data before it goes into the model.

Explain how to create a custom transformer in scikit-learn and why it is useful.

Think about the methods needed and the role in data preparation.

Describe the role of custom transformers in a machine learning pipeline and how they improve model building.

Consider the flow of data from raw to model-ready.