What does "Synthetic Minority Oversampling Technique" mean?
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The Synthetic Minority Oversampling Technique, commonly known as SMOTE, is a method used to improve the performance of models when dealing with imbalanced datasets. In many situations, we have a lot of examples of one type of data and only a few examples of another type. This imbalance can lead to models that do not perform well, especially for the less common data.
How SMOTE Works
SMOTE helps by creating new examples of the minority class. Instead of just copying existing examples, it generates new ones by looking at the existing data points and making small changes to them. This increases the number of examples for the minority class, helping models learn better from the full range of data.
Benefits of SMOTE
By using SMOTE, we can balance the dataset, which means the models can learn more effectively. This often leads to better accuracy in predictions and helps reduce errors in identifying the less common types of data. It is especially useful in fields like intrusion detection and automatic speech recognition, where certain categories might be underrepresented.