Custom transformers in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple custom transformer

What is the output of the following code when transforming the input data?

ML Python

from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np

class MultiplyByTwo(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X * 2

transformer = MultiplyByTwo()
input_data = np.array([[1, 2], [3, 4]])
output = transformer.transform(input_data)
print(output)

A

[[1 2]
 [3 4]]

B TypeError

C

[[0.5 1]
 [1.5 2]]

D

[[2 4]
 [6 8]]

💡 Hint

Think about what multiplying by two does to each element in the array.

The transform method multiplies each element by 2, so the output doubles each input value.

❓ Model Choice

intermediate

Choosing the right method to implement in a custom transformer

Which method must be implemented in a custom transformer to apply a transformation to the data?

A transform

B score

C predict

D fit_transform

💡 Hint

This method changes the data without learning from it.

The transform method applies the transformation to the input data. fit is for learning parameters, transform applies changes.

❓ Hyperparameter

advanced

Effect of a hyperparameter in a custom transformer

Consider a custom transformer that adds a constant value to all features. What effect does changing the constant hyperparameter have?

A It changes the amount added to each feature during transformation.

B It changes the number of features in the output.

C It changes the data type of the output features.

D It changes the order of features in the output.

💡 Hint

Think about what adding a constant means for each feature value.

The constant hyperparameter controls how much is added to each feature value during transform.

🔧 Debug

advanced

Debugging a custom transformer that fails to transform

Why does this custom transformer raise an error when calling transform? class AddOne(BaseEstimator, TransformerMixin): def fit(self, X, y=None): return self def transform(self, X): return X + 1 transformer = AddOne() transformer.transform([1, 2, 3])

A ValueError because fit was not called before transform

B AttributeError because transform method is missing

C TypeError because input is a list, not a numpy array

D No error, output is [2, 3, 4]

💡 Hint

Check the input type and what adding 1 means for a list.

Adding 1 to a list causes a TypeError because list + int is invalid. Converting input to numpy array fixes this.

🧠 Conceptual

expert

Why implement a custom transformer in a machine learning pipeline?

What is the main advantage of creating a custom transformer for a machine learning pipeline?

A To avoid using any external libraries for data processing

B To reuse specific data processing steps consistently and integrate them seamlessly in pipelines

C To replace the model training step with a simpler approach

D To automatically tune hyperparameters during training

💡 Hint

Think about how pipelines help organize repeated steps.

Custom transformers allow you to package data processing logic so it can be reused and combined with other steps in a pipeline.

Practice

1. What is the main purpose of creating a custom transformer in machine learning pipelines?

easy

A. To train a machine learning model directly

B. To define a reusable data processing step with fit and transform methods

C. To visualize data distributions

D. To store the final predictions of a model

Solution

1. Step 1: Understand the role of transformers

   Transformers process data by learning parameters in fit and applying changes in transform.

2. Step 2: Identify the purpose of custom transformers

   Custom transformers let you create your own data processing steps reusable in pipelines.

3. Final Answer:

   To define a reusable data processing step with fit and transform methods -> Option B

4. Quick Check:

   Custom transformer = reusable data step [OK]

Hint: Custom transformers handle data prep, not model training [OK]

Common Mistakes:

• Confusing transformers with models
• Thinking transformers visualize data
• Assuming transformers store predictions

2. Which of the following is the correct way to start defining a custom transformer class in Python using scikit-learn?

easy

A. class MyTransformer(Pipeline):

B. class MyTransformer(Model):

C. class MyTransformer(BaseEstimator, TransformerMixin):

D. def MyTransformer():

Solution

1. Step 1: Recall inheritance for custom transformers

   Custom transformers inherit from BaseEstimator and TransformerMixin to get fit and transform methods.

2. Step 2: Match correct class definition syntax

   class MyTransformer(BaseEstimator, TransformerMixin): correctly shows class inheritance from BaseEstimator and TransformerMixin.

3. Final Answer:

   class MyTransformer(BaseEstimator, TransformerMixin): -> Option C

4. Quick Check:

   Inheritance from BaseEstimator and TransformerMixin = class MyTransformer(BaseEstimator, TransformerMixin): [OK]

Hint: Custom transformers inherit BaseEstimator and TransformerMixin [OK]

Common Mistakes:

• Using Model or Pipeline as base classes
• Defining transformer as a function
• Missing inheritance entirely

3. Given this custom transformer code snippet, what will print(transformed_data) output?

from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np

class AddConstant(BaseEstimator, TransformerMixin):
    def __init__(self, constant=1):
        self.constant = constant
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X + self.constant

X = np.array([[1, 2], [3, 4]])
transformer = AddConstant(constant=5)
transformed_data = transformer.fit_transform(X)
print(transformed_data)

medium

A. [[6 7]
[8 9]]

B. [[1 2]
[3 4]]

C. [[5 5]
[5 5]]

D. Error: fit_transform method not defined

Solution

1. Step 1: Understand transform method behavior

   The transform method adds the constant (5) to every element in X.

2. Step 2: Calculate transformed data

   Original X is [[1,2],[3,4]]. Adding 5 gives [[6,7],[8,9]].

3. Final Answer:

   [[6 7] [8 9]] -> Option A

4. Quick Check:

   Adding constant 5 to X = [[6 7] [8 9]] [OK]

Hint: transform adds constant to all elements [OK]

Common Mistakes:

• Thinking fit_transform is missing
• Forgetting to add constant
• Confusing output with original data

4. What is wrong with this custom transformer code?

from sklearn.base import BaseEstimator, TransformerMixin

class MultiplyTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, factor=2):
        self.factor = factor
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X * self.factor

transformer = MultiplyTransformer(factor=3)
result = transformer.transform([1, 2, 3])
print(result)

medium

A. transform method should convert input to numpy array before multiplying

B. fit method is missing a return statement

C. factor should be a list, not an int

D. Class should inherit from Pipeline, not BaseEstimator

Solution

1. Step 1: Check input type handling in transform

   Input is a list, multiplying list by int repeats list instead of element-wise multiply.

2. Step 2: Fix transform to convert input to numpy array

   Converting input to numpy array allows element-wise multiplication as intended.

3. Final Answer:

   transform method should convert input to numpy array before multiplying -> Option A

4. Quick Check:

   List * int repeats list, need numpy array for element-wise multiply [OK]

Hint: Use numpy arrays for element-wise math in transform [OK]

Common Mistakes:

• Assuming list * int does element-wise multiply
• Missing return in fit method (actually present)
• Wrong base class inheritance

5. You want to create a custom transformer that replaces missing values in a dataset with the median of each column, then scales the data by dividing by the max value per column. Which approach correctly combines these steps in one transformer?

hard

A. In fit, replace missing values; in transform, compute medians and max values

B. Use two separate transformers instead of one custom transformer

C. Only implement transform method to do all steps without fit

D. In fit, compute medians and max values; in transform, replace missing with medians and divide by max values

Solution

1. Step 1: Understand fit and transform roles

   fit calculates statistics (median, max) from training data; transform applies these to new data.

2. Step 2: Apply correct sequence in methods

   In fit, compute medians and max values; in transform, replace missing with medians and divide by max values correctly computes medians and max in fit, then replaces missing and scales in transform.

3. Final Answer:

   In fit, compute medians and max values; in transform, replace missing with medians and divide by max values -> Option D

4. Quick Check:

   fit learns stats, transform applies them [OK]

Hint: fit learns stats; transform applies them to data [OK]

Common Mistakes:

• Doing data replacement in fit instead of transform
• Skipping fit method
• Using separate transformers unnecessarily