Label encoding in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of label encoding with unseen category

What will be the output of the following code snippet when label encoding is applied to a test set containing an unseen category?

ML Python

from sklearn.preprocessing import LabelEncoder

train_labels = ['cat', 'dog', 'fish']
test_labels = ['dog', 'cat', 'bird']

le = LabelEncoder()
le.fit(train_labels)
encoded_test = le.transform(test_labels)
print(encoded_test.tolist())

A [1, 0, 0]

B Raises a ValueError due to unseen label 'bird'

C [1, 0, 2]

D [2, 1, 0]

💡 Hint

Think about what happens when the label encoder sees a label it did not learn during fitting.

LabelEncoder cannot transform labels that were not seen during fitting. The unseen label 'bird' causes a ValueError.

🧠 Conceptual

intermediate

Purpose of label encoding in machine learning

Why do we use label encoding for categorical data in machine learning?

A To create new features by combining existing ones

B To normalize numeric features between 0 and 1

C To reduce the size of the dataset by removing categories

D To convert categorical labels into numeric form so algorithms can process them

💡 Hint

Machine learning models usually require numbers, not words.

Label encoding converts categories into numbers so models can understand and process the data.

❓ Metrics

advanced

Effect of label encoding on model accuracy

You have a classification model trained on label encoded target labels. If the label encoding mapping changes between training and testing, what is the most likely effect on model accuracy?

A Accuracy will drop significantly due to label mismatch

B Accuracy will improve because of new label mapping

C Accuracy will randomly fluctuate without pattern

D Accuracy remains unchanged as encoding does not affect labels

💡 Hint

Think about how the model interprets numeric labels during prediction.

If label encoding changes, the model's predictions will not match the true labels, causing accuracy to drop.

🔧 Debug

advanced

Identify the error in label encoding usage

What error will the following code produce and why?

ML Python

from sklearn.preprocessing import LabelEncoder

labels = ['red', 'green', 'blue']
le = LabelEncoder()
encoded = le.transform(labels)
print(encoded.tolist())

A Raises a ValueError because labels contain strings

B Prints [0, 1, 2] as labels are encoded correctly

C Raises a NotFittedError because transform is called before fit

D Prints an empty list because labels are not fitted

💡 Hint

Check the order of method calls for LabelEncoder.

LabelEncoder requires fit() before transform(). Calling transform() first raises NotFittedError.

❓ Model Choice

expert

Choosing encoding method for ordinal categorical feature

You have a categorical feature representing education levels: ['High School', 'Bachelor', 'Master', 'PhD']. Which encoding method is best to preserve the order information for a machine learning model?

A Label encoding to assign increasing integers to levels

B One-hot encoding to create separate binary columns

C Random encoding to assign random numbers to categories

D Frequency encoding to replace categories with their counts

💡 Hint

Think about preserving the natural order of categories.

Label encoding preserves order by assigning increasing integers, which helps models understand ordinal relationships.

Practice

1. What is the main purpose of label encoding in machine learning?

easy

A. Convert categorical labels into numbers for model input

B. Normalize numerical data to a 0-1 range

C. Split data into training and testing sets

D. Reduce the number of features in the dataset

Solution

1. Step 1: Understand label encoding function

   Label encoding changes categories like 'red', 'blue' into numbers like 0, 1 so models can process them.

2. Step 2: Compare with other options

   Normalization scales numbers, splitting divides data, and feature reduction removes features, none are label encoding.

3. Final Answer:

   Convert categorical labels into numbers for model input -> Option A

4. Quick Check:

   Label encoding = Convert categories to numbers [OK]

Hint: Label encoding turns words into numbers for models [OK]

Common Mistakes:

• Confusing label encoding with normalization
• Thinking label encoding splits data
• Mixing label encoding with feature selection

2. Which of the following is the correct way to import and use LabelEncoder from scikit-learn in Python?

easy

A. from sklearn import LabelEncoder
encoded = LabelEncoder.fit(['cat', 'dog', 'cat'])

B. import LabelEncoder from sklearn
encoded = LabelEncoder(['cat', 'dog', 'cat'])

C. from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoded = encoder.fit_transform(['cat', 'dog', 'cat'])

D. from sklearn.preprocessing import LabelEncoder
encoded = LabelEncoder.transform(['cat', 'dog', 'cat'])

Solution

1. Step 1: Check import syntax

   The correct import is from sklearn.preprocessing import LabelEncoder.

2. Step 2: Check usage of fit_transform

   LabelEncoder requires creating an instance, then calling fit_transform on data.

3. Final Answer:

   from sklearn.preprocessing import LabelEncoder encoder = LabelEncoder() encoded = encoder.fit_transform(['cat', 'dog', 'cat']) -> Option C

4. Quick Check:

   Correct import and fit_transform usage [OK]

Hint: Import from sklearn.preprocessing and use fit_transform() [OK]

Common Mistakes:

• Wrong import path for LabelEncoder
• Calling transform without fit
• Using LabelEncoder as a function directly

3. What will be the output of this Python code using LabelEncoder?

from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
labels = ['apple', 'banana', 'apple', 'orange']
encoded_labels = encoder.fit_transform(labels)
print(list(encoded_labels))

medium

A. [0, 1, 0, 2]

B. [1, 2, 1, 3]

C. [0, 0, 1, 2]

D. [1, 0, 1, 2]

Solution

1. Step 1: Identify unique labels and their order

   Unique labels sorted alphabetically are ['apple', 'banana', 'orange'].

2. Step 2: Assign numbers based on alphabetical order

   'apple' = 0, 'banana' = 1, 'orange' = 2, so encoded list is [0,1,0,2].

3. Final Answer:

   [0, 1, 0, 2] -> Option A

4. Quick Check:

   Alphabetical order encoding = [0,1,0,2] [OK]

Hint: LabelEncoder assigns numbers alphabetically [OK]

Common Mistakes:

• Assuming order of appearance instead of alphabetical
• Mixing up label indices
• Forgetting to convert to list before printing

4. You run this code but get an error:

from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
labels = ['red', 'blue', 'green']
encoded = encoder.transform(labels)
print(encoded)

What is the problem?

medium

A. transform() only works on numbers, not strings

B. LabelEncoder cannot encode color names

C. You should import LabelEncoder from sklearn.preprocessing.label

D. You must call fit or fit_transform before transform

Solution

1. Step 1: Understand LabelEncoder usage

   LabelEncoder requires fitting on data before transforming new data.

2. Step 2: Identify missing fit step

   The code calls transform without fit or fit_transform, causing error.

3. Final Answer:

   You must call fit or fit_transform before transform -> Option D

4. Quick Check:

   fit before transform = required [OK]

Hint: Always fit before transform with LabelEncoder [OK]

Common Mistakes:

• Calling transform without fitting first
• Wrong import path
• Thinking transform works on raw strings directly

5. You have a dataset with a categorical feature 'Fruit' containing ['apple', 'banana', 'apple', 'banana', 'orange', 'banana']. You want to encode it for a model that treats numbers as ordered values. Which approach is best?

hard

A. Use LabelEncoder to assign numbers (0,1,2) to fruits

B. Manually assign numbers based on fruit sweetness order

C. Use OneHotEncoder to create separate binary columns for each fruit

D. Leave the feature as text because encoding is not needed

Solution

1. Step 1: Understand model needs for ordered values

   The model treats numbers as ordered, so encoding must reflect meaningful order.

2. Step 2: Evaluate encoding options

   LabelEncoder assigns arbitrary numbers alphabetically, OneHotEncoder creates separate columns without order, manual assignment can reflect sweetness order.

3. Step 3: Choose best approach

   Manual assignment based on domain knowledge preserves order, fitting model assumptions.

4. Final Answer:

   Manually assign numbers based on fruit sweetness order -> Option B

5. Quick Check:

   Ordered encoding needs meaningful number assignment [OK]

Hint: Assign numbers reflecting real order for ordered models [OK]

Common Mistakes:

• Using LabelEncoder blindly for ordered data
• Confusing one-hot with ordered encoding
• Ignoring model assumptions about number meaning