SMOTE is an oversampling technique that addresses the issue of imbalanced datasets in classification problems123. It works by creating synthetic examples from the minority class123.

How SMOTE Works

• SMOTE selects examples that are close in the feature space4.
• It draws a line between the examples in the feature space4.
• It draws a new sample at a point along that line4.
• It generates the virtual training records by linear interpolation for the minority class5.

Benefits of SMOTE

• SMOTE helps to balance the class distribution of the dataset1.
• It can improve the performance of machine learning models12.
• It supports many classifiers, including decision trees, support vector machines, nearest neighbors, and neural networks2.

Limitations of SMOTE

• SMOTE can create synthetic data points that may not be very realistic6.
• It can increase the variance of machine learning models6.
• It can be computationally expensive to generate a lot of synthetic data points6.

Use Cases of SMOTE

• SMOTE is used in fraud detection where the number of fraudulent transactions is usually much lower than the number of genuine transactions2.
• It is used in medical diagnosis where certain diseases might be rare, and thus the number of positive cases is much lower than the number of negative cases2.
• It is used in various domains where the data is imbalanced5.