Learn to Handle Imbalanced Data

Edition #145 | 1 June 2025

Jun 02, 2025

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Welcome to today's edition of Business Analytics Review!

Today, we’re diving into a common challenge that many data scientists face: Handling Imbalanced Data. Whether you’re detecting fraud, diagnosing rare diseases, or predicting customer churn, imbalanced data can skew your models and lead to misleading results. Let’s explore what this means and how to address it with three powerful techniques: class weighting, SMOTE, and ADASYN.

Today, we’re diving into a common challenge that many data scientists face: handling imbalanced data. Whether you’re detecting fraud, diagnosing rare diseases, or predicting customer churn, imbalanced data can skew your models and lead to misleading results. Let’s explore what this means and how to address it with three powerful techniques: class weighting, SMOTE, and ADASYN.

The Challenge of Imbalanced Data

Imagine you’re a data scientist at a bank, tasked with building a model to detect fraudulent transactions. Your dataset contains millions of transactions, but only a tiny fraction—say, 1%—are fraudulent. If you train a model on this data without any adjustments, it might learn to predict “not fraud” for nearly every transaction, achieving high accuracy but failing to catch the frauds you care about most. This scenario highlights the problem of imbalanced data, where one class (the majority) significantly outnumbers another (the minority).

Imbalanced datasets are common in real-world applications, such as:

Fraud detection: Most transactions are legitimate, with fraud being rare.
Medical diagnosis: Conditions like certain cancers are far less common than healthy cases.
Ad click prediction: Only a small percentage of users click on online ads.

When a model is trained on such data, it often becomes biased toward the majority class, leading to poor performance on the minority class, which is typically the class of interest. This can have serious consequences, like missing fraudulent activities or failing to diagnose a critical illness.

Why Imbalanced Data is a Problem

Machine learning algorithms aim to minimize errors across all predictions, but with imbalanced data, they can achieve high accuracy by simply predicting the majority class every time. For instance, in a dataset with 99% non-fraudulent transactions, a model that always predicts “non-fraud” will be 99% accurate but useless for detecting fraud. This bias occurs because the model sees far more examples of the majority class during training, learning patterns that favor it over the minority class.

To evaluate models on imbalanced data, accuracy alone is misleading. Instead, metrics like precision, recall, F1-score, or Area Under the ROC Curve (AUC) are more appropriate, as they focus on the model’s performance on the minority class.

Techniques to Handle Imbalanced Data

Fortunately, several techniques can help balance the scales and improve model performance. Today, we’ll focus on three widely used methods: class weighting, SMOTE, and ADASYN. Each offers a unique approach to tackling imbalance, and choosing the right one depends on your dataset and problem.

1. Class Weighting

Class weighting is a straightforward technique that adjusts the model’s loss function to give more importance to the minority class. By assigning higher weights to minority class samples, the model is penalized more for misclassifying them, encouraging it to learn their patterns better.

For example, in scikit-learn, many classifiers like Logistic Regression or Random Forest support a class_weight parameter. Setting it to 'balanced' automatically assigns weights inversely proportional to class frequencies. For a dataset with 90% majority and 10% minority samples, the minority class might receive a weight 9 times higher than the majority class, ensuring the model pays more attention to it.

This method is simple to implement and doesn’t alter the dataset itself, making it a great starting point. However, it may not be sufficient for highly imbalanced datasets, where additional techniques are needed.

2. SMOTE (Synthetic Minority Over-sampling Technique)

SMOTE is a popular oversampling method that generates synthetic samples for the minority class to balance the dataset. Instead of duplicating existing minority samples (which can lead to overfitting), SMOTE creates new samples by interpolating between existing ones. It works by:

Selecting a minority class sample.
Finding its k-nearest neighbors (typically k=5) in the feature space.
Creating a new sample along the line segment between the selected sample and one of its neighbors.

For instance, in a medical dataset with few positive diagnoses, SMOTE can generate new positive cases that are plausible variations of existing ones, helping the model learn a more robust decision boundary. SMOTE is implemented in the imbalanced-learn library and is effective for many classification tasks, though it may struggle if classes overlap significantly in the feature space.

3. ADASYN (Adaptive Synthetic Sampling)

ADASYN is an advanced version of SMOTE that adaptively generates synthetic samples based on the difficulty of learning minority class instances. It focuses on areas where the minority class is sparse or surrounded by majority class samples, which are harder for models to learn. ADASYN works by:

Calculating the degree of class imbalance.
Identifying minority samples in dense majority class regions using k-nearest neighbors.
Generating more synthetic samples for these “harder-to-learn” instances.

This adaptive approach makes ADASYN particularly effective for highly imbalanced datasets, such as those in fraud detection, where the minority class is not only rare but also difficult to distinguish. Like SMOTE, ADASYN is available in the imbalanced-learn library and can be combined with other techniques for better results.

Practical Considerations

Each technique has its strengths and weaknesses:

Class Weighting: Easy to implement, works with most algorithms, but may not suffice for extreme imbalances.
SMOTE: Effective for creating balanced datasets, but can introduce noise if classes overlap.
ADASYN: Adapts to difficult regions, but may generate samples too similar to the majority class, potentially causing false positives.

Experimentation is crucial, as the best approach depends on your dataset’s characteristics and the problem at hand. Always evaluate models using appropriate metrics like F1-score or AUC, and consider combining techniques (e.g., SMOTE with class weighting) for optimal performance.

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Trending AI Tool: imbalanced-learn

This open-source Python package, built on top of scikit-learn, offers a wide range of methods for handling imbalanced datasets, including SMOTE, ADASYN, random undersampling, and more. Its user-friendly API and extensive documentation make it an essential tool for data scientists tackling class imbalance. Whether you’re working on fraud detection or medical diagnostics, imbalanced-learn can streamline your preprocessing pipeline and improve model performance.
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