Undersampling Vs Oversampling Vs Smote

Undersampling vs Oversampling vs SMOTE: Which Technique is Best for Handling Imbalanced Data?IntroductionIn machine learning, one of the most significant challenges is dealing with imbalanced datasets. When working with imbalanced data, the model may become biased toward the majority class, leading to poor performance on the minority class. To combat this, techniques like undersampling, oversampling, and SMOTE (Synthetic Minority Over-sampling Technique) are widely used. But which technique is the best for your dataset? In this topic, we will compare these three methods undersampling, oversampling, and SMOTE to help you decide which approach will improve your model’s performance.

Table of Contents

What is Imbalanced Data?

Imbalanced data refers to a situation where the classes in a dataset are not represented equally. In a binary classification problem, for example, the number of instances in one class might be much larger than in the other class. This can lead to a situation where the model learns to predict the majority class more effectively than the minority class, resulting in a skewed model performance.

For example, in fraud detection, fraudulent transactions (minority class) are far less common than legitimate transactions (majority class). When trained on an imbalanced dataset, the model may fail to accurately detect fraudulent transactions, as it is biased toward predicting the majority class.

What is Undersampling?

Undersampling Overview

Undersampling is a technique used to balance the class distribution in a dataset by reducing the number of instances in the majority class. This process involves randomly selecting a subset of the majority class to match the size of the minority class. While this method can effectively balance the dataset, it has some drawbacks.

How Undersampling Works

In undersampling, the majority class is reduced by randomly removing samples. This results in a more balanced dataset, where the number of samples in both classes is approximately equal. However, this method may cause valuable information to be discarded, leading to a loss of important patterns in the data.

Advantages of Undersampling

Simple and Fast: Undersampling is straightforward and computationally less expensive since it does not involve generating new samples or performing complex operations.
Balanced Dataset: It creates a balanced dataset, which can help the model avoid bias toward the majority class.

Disadvantages of Undersampling

Information Loss: By discarding samples from the majority class, undersampling may lead to the loss of valuable information that could help the model generalize better.
Underfitting Risk: Reducing the number of samples from the majority class may cause the model to underfit, as it has less data to learn from.

What is Oversampling?

Oversampling Overview

Oversampling, as the name suggests, is a technique used to address imbalanced data by increasing the number of instances in the minority class. The goal of oversampling is to balance the dataset by duplicating instances from the minority class or generating new synthetic data points. This method ensures that the model has sufficient data to learn from, especially for the minority class.

How Oversampling Works

In oversampling, the minority class is augmented by either duplicating existing samples or generating synthetic samples through various techniques. The idea is to make the class distribution more balanced, allowing the model to learn patterns from the minority class as well.

Advantages of Oversampling

Increased Model Accuracy: By increasing the number of minority class instances, oversampling improves the model’s ability to learn from these instances, leading to better accuracy for the minority class.
No Data Loss: Unlike undersampling, oversampling does not result in the loss of information from the majority class.

Disadvantages of Oversampling

Overfitting Risk: Oversampling can lead to overfitting, especially when the same minority class samples are duplicated. The model may learn to memorize the repeated instances instead of generalizing to unseen data.
Computational Cost: Depending on the size of the dataset, oversampling can increase computational costs, as it requires generating or duplicating samples.

What is SMOTE?

SMOTE Overview

SMOTE (Synthetic Minority Over-sampling Technique) is an advanced technique designed to overcome the limitations of basic oversampling. Instead of simply duplicating existing samples, SMOTE generates synthetic samples by interpolating between existing instances of the minority class. This approach ensures that the generated data points are not identical to the original data, helping to prevent overfitting.

How SMOTE Works

SMOTE works by selecting a minority class sample and finding its nearest neighbors in the feature space. It then generates synthetic samples by creating new data points along the line segments between the sample and its neighbors. This helps to create more varied synthetic samples, improving the diversity of the minority class data.

Advantages of SMOTE

No Information Loss: Unlike undersampling, SMOTE does not discard any data from the majority class, preserving all information.
Prevents Overfitting: By generating new synthetic samples instead of duplicating existing ones, SMOTE helps to reduce the risk of overfitting.
Improves Model Generalization: SMOTE generates synthetic samples that are likely to be in the same distribution as the original data, improving the model’s ability to generalize.

Disadvantages of SMOTE

Complexity: SMOTE is computationally more complex than basic oversampling or undersampling, as it involves generating synthetic data points and computing nearest neighbors.
Possible Noise: While SMOTE generates more data points, some of them may be noisy or not truly representative of the minority class, especially if the original data is very sparse.

SMOTE vs Oversampling vs Undersampling: Key Differences

1. Data Augmentation

Undersampling: Reduces the number of samples in the majority class to balance the dataset.
Oversampling: Increases the number of minority class samples by duplicating or generating synthetic data.
SMOTE: Creates synthetic samples by interpolating between existing minority class instances, providing more variety than basic oversampling.

2. Information Retention

Undersampling: May lose valuable information by removing samples from the majority class.
Oversampling: Does not lose any information, as it only adds more data.
SMOTE: Also retains all information, while generating synthetic samples that reflect the original data distribution.

3. Overfitting Risk

Undersampling: Low risk of overfitting, but may result in underfitting due to a smaller dataset.
Oversampling: Higher risk of overfitting due to the repetition of minority class samples.
SMOTE: Lower risk of overfitting compared to simple oversampling because it generates unique synthetic samples.

4. Computational Complexity

Undersampling: Simple and computationally inexpensive.
Oversampling: Can increase computational costs, especially for large datasets.
SMOTE: More computationally intensive than both undersampling and basic oversampling due to the generation of synthetic samples.

When to Use Each Technique

Use Undersampling: When you have a large dataset with an overwhelming majority class and can afford to remove some data without losing essential patterns. It’s ideal when you want to simplify the problem and reduce the model’s computational cost.
Use Oversampling: When you want to retain all the data from the majority class and improve the model’s learning of the minority class without sacrificing information. It works best when you have a relatively small dataset and can afford the increased computational cost.
Use SMOTE: When you want to create synthetic instances of the minority class to enhance model performance while avoiding overfitting. SMOTE is particularly useful for handling class imbalance in datasets with complex features, as it creates more diverse and varied data points.

Handling imbalanced data is a crucial step in building robust machine learning models. Whether to choose undersampling, oversampling, or SMOTE depends on your dataset’s characteristics and the specific challenges you face. Each technique has its pros and cons, and understanding them will help you make an informed decision. By applying the right technique, you can improve your model’s accuracy, reduce bias, and ultimately build a more reliable model for imbalanced classification tasks.