Imbalanced class handling (SMOTE, class weights) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use SMOTE for imbalanced data?

Imagine you have a dataset where one class is much smaller than the other. Why would you use SMOTE (Synthetic Minority Over-sampling Technique) instead of just duplicating minority class samples?

A SMOTE creates new synthetic samples by mixing existing minority samples, which helps the model learn better decision boundaries.

B SMOTE removes majority class samples to balance the dataset, reducing training time.

C SMOTE duplicates minority samples exactly to increase their count without changing data distribution.

D SMOTE randomly deletes samples from both classes to balance the dataset.

💡 Hint

Think about how creating new data points can help the model generalize better than just copying existing ones.

SMOTE generates new synthetic minority class samples by interpolating between existing ones. This helps the model learn a more general decision boundary rather than memorizing duplicates.

❓ Predict Output

intermediate

Output of class weight usage in logistic regression

What will be the output of the following code snippet regarding the model's class weight attribute?

ML Python

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(class_weight={0:1, 1:5})
print(model.class_weight)

A {0: 1, 1: 5}

B balanced

C None

D Raises TypeError

💡 Hint

Check what value is assigned to class_weight in the model initialization.

The class_weight parameter is explicitly set to a dictionary {0:1, 1:5}, so printing model.class_weight will output this dictionary.

❓ Metrics

advanced

Choosing the best metric for imbalanced classification

You trained a model on a dataset with 95% of class 0 and 5% of class 1. Which metric is best to evaluate your model's performance on the minority class?

A Precision for class 1, to measure how many predicted positives are correct.

B Mean Squared Error, because it measures prediction error.

C Accuracy, because it shows overall correct predictions.

D Recall for class 1, to measure how many actual positives are found.

💡 Hint

Think about which metric helps find most of the minority class samples.

Recall for the minority class measures how many actual positive samples are correctly identified, which is crucial in imbalanced data to avoid missing minority cases.

🔧 Debug

advanced

Why does this SMOTE code raise an error?

What error will this code raise and why? from imblearn.over_sampling import SMOTE X = [[1,2],[3,4],[5,6]] y = [0,0,1] smote = SMOTE(sampling_strategy='minority') X_res, y_res = smote.fit_resample(X, y)

A ValueError: Expected 2D array, got 1D array instead.

B No error, code runs successfully.

C ValueError: At least 6 samples are needed to perform SMOTE.

D TypeError: 'list' object has no attribute 'fit'.

💡 Hint

SMOTE needs enough samples in the minority class to create synthetic samples.

SMOTE requires at least k_neighbors+1 samples in the minority class to generate synthetic samples. Here, only one minority sample exists, so it raises a ValueError.

❓ Model Choice

expert

Best approach for highly imbalanced multi-class classification

You have a multi-class dataset with 4 classes, where one class is only 1% of data. You want to improve model performance on the rare class. Which approach is best?

A Apply SMOTE oversampling to all classes equally before training.

B Use class weights in the model to give higher importance to the rare class.

C Remove the rare class samples to simplify the problem.

D Use accuracy as the only metric to evaluate the model.

💡 Hint

Think about how to handle imbalance without losing data or misleading metrics.

Using class weights allows the model to pay more attention to the rare class during training without changing data distribution or removing samples.

Practice

1. What is the main purpose of using SMOTE in machine learning?

easy

A. To create synthetic samples for minority classes to balance the dataset

B. To reduce the size of the majority class by removing samples

C. To increase the number of features in the dataset

D. To randomly shuffle the dataset before training

Solution

1. Step 1: Understand SMOTE's role in imbalanced data

   SMOTE stands for Synthetic Minority Over-sampling Technique and it creates new synthetic samples for the minority class.

2. Step 2: Compare options with SMOTE's function

   Only To create synthetic samples for minority classes to balance the dataset correctly describes SMOTE's purpose to balance classes by adding synthetic minority samples.

3. Final Answer:

   To create synthetic samples for minority classes to balance the dataset -> Option A

4. Quick Check:

   SMOTE = Synthetic samples for minority [OK]

Hint: SMOTE = make new minority samples to balance [OK]

Common Mistakes:

• Thinking SMOTE removes majority samples
• Confusing SMOTE with feature engineering
• Assuming SMOTE shuffles data

2. Which of the following is the correct way to set class weights in scikit-learn's LogisticRegression?

easy

A. LogisticRegression(class_weight='balanced')

B. LogisticRegression(weight_class='balanced')

C. LogisticRegression(classweights='balanced')

D. LogisticRegression(weights='balanced')

Solution

1. Step 1: Recall scikit-learn parameter for class weights

   The correct parameter name is class_weight and it accepts 'balanced' to auto-adjust weights.

2. Step 2: Match options with correct syntax

   Only LogisticRegression(class_weight='balanced') uses the exact parameter class_weight='balanced'.

3. Final Answer:

   LogisticRegression(class_weight='balanced') -> Option A

4. Quick Check:

   Parameter name is class_weight [OK]

Hint: Use class_weight='balanced' exactly in model init [OK]

Common Mistakes:

• Using wrong parameter names like weight_class
• Misspelling class_weight
• Passing weights instead of class_weight

3. Given this code snippet using SMOTE, what will be the shape of X_resampled and y_resampled?

from imblearn.over_sampling import SMOTE
X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)
print(len(X_resampled), len(y_resampled))

medium

A. 8 8

B. 6 6

C. 10 10

D. 12 12

Solution

1. Step 1: Count original class samples

   Class 0 has 3 samples, class 1 has 3 samples, so dataset is balanced initially.

2. Step 2: Understand SMOTE behavior on balanced data

   SMOTE will create synthetic samples to balance minority class to majority class size. Here both classes are equal, so no new samples are needed.

3. Step 3: Check actual output

   Since classes are equal, no new samples are added. So output length remains 6.

4. Final Answer:

   6 6 -> Option B

5. Quick Check:

   Balanced classes, no new samples added [OK]

Hint: SMOTE adds samples only if classes are imbalanced [OK]

Common Mistakes:

• Assuming SMOTE always doubles data
• Ignoring original class counts
• Confusing sample count with feature count

4. You wrote this code to apply class weights but the model accuracy is very low. What is the likely error?

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(class_weight={'0':1, '1':10})
model.fit(X_train, y_train)

medium

A. LogisticRegression does not support class weights

B. class_weight parameter does not accept dictionaries

C. Class weights keys should be integers, not strings

D. class_weight values must sum to 1

Solution

1. Step 1: Check class_weight dictionary keys

   Class labels in class_weight must match label types in y_train. Usually labels are integers 0 and 1, not strings '0' and '1'.

2. Step 2: Understand impact of wrong keys

   If keys are strings but labels are integers, weights won't apply correctly, causing poor model performance.

3. Final Answer:

   Class weights keys should be integers, not strings -> Option C

4. Quick Check:

   Keys must match label types [OK]

Hint: Match class_weight keys to label data types exactly [OK]

Common Mistakes:

• Using string keys instead of integer keys
• Thinking class_weight can't be a dict
• Believing weights must sum to 1

5. You have a dataset with 95% class 0 and 5% class 1. You want to train a model that handles this imbalance. Which approach is best to improve minority class recall?

hard

A. Train the model without any imbalance handling

B. Only use SMOTE without changing class weights

C. Only set class_weight='balanced' without oversampling

D. Use SMOTE to create synthetic minority samples and set class_weight='balanced' in the model

Solution

1. Step 1: Understand dataset imbalance

   With 95% vs 5%, the minority class is very small and model may ignore it.

2. Step 2: Combine SMOTE and class weights

   SMOTE creates synthetic minority samples to balance data, while class_weight='balanced' tells model to focus more on minority class during training.

3. Step 3: Why combining is best

   Using both together improves minority recall better than using either alone or ignoring imbalance.

4. Final Answer:

   Use SMOTE to create synthetic minority samples and set class_weight='balanced' in the model -> Option D

5. Quick Check:

   Combine oversampling + class weights for best minority recall [OK]

Hint: Combine SMOTE and class_weight='balanced' for best results [OK]

Common Mistakes:

• Using only one method and expecting best recall
• Ignoring imbalance completely
• Assuming oversampling alone fixes all issues