Random Undersampling

1. Definition

• Random Undersampling = a resampling technique for handling class imbalance in classification tasks.
• Idea: reduce the number of samples in the majority class by randomly removing them until class distribution is more balanced.

2. Why It’s Used

• In imbalanced datasets (e.g., fraud detection, churn prediction), the majority class overwhelms the minority class.
• Classifiers may become biased toward the majority.
• Undersampling helps by making classes more balanced.

3. How It Works

Example: Binary classification

• Majority class (non-fraud): 10,000 samples
• Minority class (fraud): 1,000 samples

Random undersampling:

• Randomly select 1,000 samples from the majority class
• New dataset = 1,000 majority + 1,000 minority

4. Advantages

• Simple and fast
• Balances dataset → models don’t ignore the minority class
• Reduces training time (fewer samples)

5. Disadvantages

• Information loss: many potentially useful majority samples are discarded
• Risk of underfitting, since the model trains on fewer data points
• Works poorly if minority class is extremely small (too much data discarded)

6. Alternatives / Variants

• Random Oversampling: duplicate minority samples
• SMOTE (Synthetic Minority Over-sampling Technique): generate synthetic minority samples
• Tomek Links / Edited Nearest Neighbors: smarter undersampling (removes borderline/overlapping majority samples instead of random)
• Ensemble methods: combine undersampling with bagging/boosting (e.g., Balanced Random Forest).

7. Example (Python, imbalanced-learn)

from imblearn.under_sampling import RandomUnderSampler
from collections import Counter

X, y = ...  # features and labels
print("Original distribution:", Counter(y))

rus = RandomUnderSampler(random_state=42)
X_res, y_res = rus.fit_resample(X, y)

print("Resampled distribution:", Counter(y_res))

from imblearn.under_sampling import RandomUnderSampler
from collections import Counter

X, y = ...  # features and labels
print("Original distribution:", Counter(y))

rus = RandomUnderSampler(random_state=42)
X_res, y_res = rus.fit_resample(X, y)

print("Resampled distribution:", Counter(y_res))

Summary

• Random Undersampling = randomly drop majority class samples to balance dataset.
• Pros: simple, faster training, balances data.
• Cons: loses information, risk of underfitting.
• Often combined with oversampling or ensemble methods for better results.