Synthetic Minority Over-sampling Technique.

What is SMOTE?

SMOTE (Synthetic Minority Oversampling Technique) is a widely used approach to synthesizing new examples in machine learning. It was introduced by Nitesh Chawla and his research team in their 2002 paper titled “SMOTE: Synthetic Minority Over-sampling Technique.”

How does SMOTE work?

SMOTE works by generating synthetic examples in the feature space of a dataset. It creates new examples by selecting the minority class samples that are close to each other and creating synthetic data points along the line between them. This allows the generation of new examples that can be used to balance the class distribution of a dataset.

Why is SMOTE used?

SMOTE is used to address class imbalance, where the number of samples in each class of a dataset is not equal. In a dataset with unequal classes, a problem arises where the classifier may predict all examples as belonging to the majority class, since this will give a high accuracy rate. SMOTE addresses this by artificially increasing the number of minority class examples and, therefore, improving the accuracy of the classifier for both minority and majority classes.

Advantages of SMOTE

SMOTE is a quick and effective way to create synthetic examples for the minority class. It is easy to implement and has been shown to improve the performance of classifiers in many real-world applications. SMOTE also helps to reduce or eliminate the bias towards the majority class that can occur in datasets with class imbalance.

Disadvantages of SMOTE

While SMOTE is an effective way to balance class distribution, it can also lead to overfitting of the model. This happens when synthetic examples are too similar to each other, causing the model to rely on a small number of points in the feature space. Additionally, SMOTE does not capture the true distribution of the minority class and may create synthetic examples that are not representative of the class.

In summary, SMOTE is a powerful and widely used technique for addressing class imbalance in datasets. It generates synthetic examples in the feature space by connecting minority class samples and creating new data points along the line. While SMOTE has its advantages, it is important to be aware of its limitations and potential drawbacks.

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