What is class imbalance?

Class imbalance occurs when the classes in a dataset have significantly different numbers of samples. For example, 95% negative samples and 5% positive samples. This causes machine learning models to be biased toward the majority class and perform poorly on minority classes.

Why is class imbalance a problem?

Models trained on imbalanced data often predict the majority class too often, ignoring minority classes. This leads to high accuracy but poor precision/recall on the minority class. In fraud detection or disease diagnosis, this is dangerous because missing positive cases is costly.

What are class weights?

Class weights are values assigned to each class that tell the model to pay more attention to underrepresented classes during training. A minority class with weight 10 is treated as if it appears 10x more often. This helps balance the decision boundary.

When should I use class weights vs oversampling?

Class weights are simpler and faster (no data duplication). Use them first for mild imbalance. Oversampling/SMOTE work better for severe imbalance but risk overfitting. Undersampling loses data. The best approach depends on your dataset size and imbalance severity.

Class Balance Analyzer

Upload or paste your class labels to analyze dataset balance. Get class distribution, imbalance severity, and calculated class weights for PyTorch, TensorFlow, and Scikit-learn.

Input Method:

📋 Paste Labels

📤 Upload CSV

Paste Labels (one per line or comma-separated):

Upload CSV File:

📁

Click to upload or drag & drop

CSV file with label column

CSV Format: First row should have a header. Example: "label,value,target" where any column can be your labels.

Understanding Class Imbalance

Imbalance Ratio: The ratio of the largest class to the smallest class. Ratio > 2 usually indicates imbalance.
Minority Class %: The percentage of the smallest class. Below 10% is typically problematic.
Severity: LOW (ratio < 2), MEDIUM (ratio 2-10), HIGH (ratio > 10).
Why it matters: Models trained on imbalanced data often ignore the minority class entirely.

Solutions for Class Imbalance

EASY Class Weights

Assign higher weights to minority classes during training. Fastest solution, works for most cases. Built into most frameworks.

👍 Pro: Simple, no data changes
👎 Con: May not work for extreme imbalance

MEDIUM Oversampling

Duplicate minority class samples or use SMOTE to generate synthetic samples. Increases training data.

👍 Pro: Effective, preserves info
👎 Con: Risk of overfitting

MEDIUM Undersampling

Remove majority class samples to balance classes. Reduces training data and speed.

👍 Pro: Faster training
👎 Con: Loses information

HARD SMOTE / Advanced Techniques

Synthetic Minority Over-sampling Technique. Creates synthetic samples between existing minority samples.

👍 Pro: Best for severe imbalance
👎 Con: Complex, requires tuning

EASY Threshold Adjustment

Change the decision threshold instead of 0.5. Trades off precision vs recall.

👍 Pro: No retraining needed
👎 Con: Only for binary classification

EASY Different Metrics

Use F1, precision-recall AUC instead of accuracy. Better for imbalanced data.

👍 Pro: Free, immediate
👎 Con: Just evaluation, not fixing

Class Weights Explained

For a binary classification with 95 positives and 5 negatives:

Balanced formula: weight = total_samples / (num_classes × class_count)
Example: Negative weight = 100 / (2 × 95) = 0.53, Positive weight = 100 / (2 × 5) = 10
Interpretation: The model treats each positive sample as 10× more important during training
Alternative: Some prefer weight = 1 / class_count, others use log scaling

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Class Balance Analyzer

Class Balance Analyzer

Class Distribution

Class Weights Calculator

Recommended Solutions

Understanding Class Imbalance

Solutions for Class Imbalance

EASY Class Weights

MEDIUM Oversampling

MEDIUM Undersampling

HARD SMOTE / Advanced Techniques

EASY Threshold Adjustment

EASY Different Metrics

Class Weights Explained

Related Tools