Custom transformers in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the base class for creating a custom transformer in scikit-learn.

ML Python

from sklearn.base import [1]

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ABaseEstimator

BTransformerMixin

CCustomTransformer

DPipeline

Attempts:

3 left

💡 Hint

Common Mistakes

Using TransformerMixin instead of BaseEstimator for inheritance.

Trying to import a non-existent class named CustomTransformer.

The BaseEstimator class is the base class to inherit from when creating custom transformers in scikit-learn.

2fill in blank

medium

Complete the code to define the fit method for a custom transformer class.

ML Python

class MyTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        # No fitting needed, just return self
        return [1]

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Aself

By

CX

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Returning the input data X instead of self.

Returning None which breaks chaining.

The fit method should return self to allow method chaining in scikit-learn.

3fill in blank

hard

Fix the error in the transform method to correctly transform input data by doubling all values.

ML Python

def transform(self, X):
    return X [1] 2

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A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using division or subtraction which changes values incorrectly.

To double all values in X, multiply by 2 using the * operator.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

ML Python

{word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) as the dictionary value.

Using less than operator < which filters wrong words.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is greater than zero.

ML Python

result = {{ [1]: [2] for word, count in word_counts.items() if count [3] 0 }}

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Aword.upper()

Bcount

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase.

Using less than operator < which filters wrong counts.

The comprehension maps the uppercase version of each word (word.upper()) to its count (count) only if the count is greater than zero (> 0).