Imbalanced class handling (SMOTE, class weights) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SMOTE class from the imblearn library.

ML Python

from imblearn.[1] import SMOTE

Drag options to blanks, or click blank then click option'

Aover_sampling

Bunder_sampling

Censemble

Dmetrics

Attempts:

3 left

💡 Hint

Common Mistakes

Importing SMOTE from the wrong module like under_sampling or metrics.

Misspelling the module name.

The SMOTE class is located in the over_sampling module of the imblearn library. This module contains techniques to increase minority class samples.

2fill in blank

medium

Complete the code to create a SMOTE object with a random state for reproducibility.

ML Python

smote = SMOTE(random_state=[1])

Drag options to blanks, or click blank then click option'

ANone

B42

C'seed'

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of an integer for random_state.

Passing None which disables reproducibility.

Setting random_state=42 ensures the SMOTE sampling is reproducible. The value 42 is commonly used as a fixed seed.

3fill in blank

hard

Fix the error in the code to fit and resample the training data using SMOTE.

ML Python

X_resampled, y_resampled = smote.[1](X_train, y_train)

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cfit_transform

Dsample

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() which returns the object but not data.

Using transform() which requires prior fitting.

The fit_transform method fits the SMOTE model and returns the resampled data. Using only fit does not return the resampled data.

4fill in blank

hard

Fill both blanks to create a logistic regression model with balanced class weights and fit it on training data.

ML Python

model = LogisticRegression(class_weight=[1])
model.[2](X_train, y_train)

Drag options to blanks, or click blank then click option'

A'balanced'

Bfit

Cpredict

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None or wrong string to class_weight.

Using predict instead of fit to train the model.

Setting class_weight='balanced' tells the model to adjust weights inversely proportional to class frequencies. Then fit trains the model on data.

5fill in blank

hard

Fill all three blanks to compute class weights manually and train a logistic regression model with them.

ML Python

from sklearn.utils.class_weight import compute_class_weight

weights = compute_class_weight(class_weight=[1], classes=[2], y=[3])
class_weights = dict(zip([2], weights))
model = LogisticRegression(class_weight=class_weights)
model.fit(X_train, y_train)

Drag options to blanks, or click blank then click option'

A'balanced'

Bnp.unique(y_train)

Cy_train

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None instead of 'balanced' to compute_class_weight.

Using wrong variables for classes or y parameters.

To compute class weights manually, use class_weight='balanced', pass the unique classes from training labels, and the training labels themselves. Then create a dictionary for the model.

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