Imbalanced class handling (SMOTE, class weights) in ML Python - Model Pipeline Trace

Model Pipeline - Imbalanced class handling (SMOTE, class weights)

This pipeline shows how to handle imbalanced classes in a dataset using SMOTE to create synthetic samples and class weights to help the model learn better from minority classes.

Data Flow - 6 Stages

1Raw data input

1000 rows x 5 columns→Original dataset with imbalanced classes (90% class 0, 10% class 1)→1000 rows x 5 columns

[Feature1=2.3, Feature2=1.1, ..., Class=0]

↓

2Train/test split

1000 rows x 5 columns→Split data into training (80%) and testing (20%) sets→Train: 800 rows x 5 columns, Test: 200 rows x 5 columns

Train sample: [Feature1=2.3, ..., Class=0], Test sample: [Feature1=1.5, ..., Class=1]

↓

3SMOTE oversampling

Train: 800 rows x 5 columns→Create synthetic minority class samples to balance classes in training set→Train: 1440 rows x 5 columns (balanced classes)

Synthetic sample: [Feature1=2.0, Feature2=1.3, ..., Class=1]

↓

4Feature scaling

Train: 1440 rows x 5 columns, Test: 200 rows x 5 columns→Fit scaler on train features and scale (transform) both train and test features to similar ranges for model training→Train: 1440 rows x 5 columns (scaled), Test: 200 rows x 5 columns (scaled)

[Feature1=0.45, Feature2=0.33, ..., Class=1]

↓

5Model training with class weights

Train: 1440 rows x 5 columns (scaled)→Train model using class weights to emphasize minority class→Trained model

Class weights: {0:1.0, 1:1.0} after SMOTE (balanced)

↓

6Model evaluation

Test: 200 rows x 5 columns (scaled)→Evaluate model performance on imbalanced test set→Performance metrics

Accuracy=0.92, Precision=0.85, Recall=0.88

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 | *      
0.5 |  *     
0.4 |   *    
0.3 |    *   
0.2 |     *  
0.1 |       
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.70	Model starts learning, loss high, accuracy moderate
2	0.48	0.80	Loss decreases, accuracy improves
3	0.35	0.87	Model learns minority class better
4	0.28	0.90	Loss continues to decrease, accuracy rises
5	0.22	0.92	Training converges with good balance

Prediction Trace - 4 Layers

Layer 1: Input features

Layer 2: Hidden layer with ReLU activation

Layer 3: Output layer with sigmoid activation

Layer 4: Threshold decision

Model Quiz - 3 Questions

What is the main purpose of SMOTE in this pipeline?

A To scale features to the same range

B To remove samples from the majority class

C To create synthetic samples for the minority class

D To split data into train and test sets

Key Insight

Handling imbalanced classes with SMOTE and class weights helps the model learn minority classes better, improving overall performance and fairness.

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