SMOTE

Introduction to SMOTE

SMOTE, or Synthetic Minority Over-sampling Technique, is a powerful method used to address class imbalance in machine learning datasets. When a dataset has a significantly smaller number of instances in one class compared to others, traditional machine learning algorithms may perform poorly on the minority class due to its under-representation. SMOTE helps to alleviate this problem by creating synthetic samples of the minority class[4].

How SMOTE Works

Selecting Minority Class Instances: SMOTE starts by selecting instances from the minority class.

Finding Nearest Neighbors: For each selected instance, it identifies its k-nearest neighbors (typically using Euclidean distance).

Generating Synthetic Samples: New synthetic instances are generated by interpolating between the selected instance and one or more of its nearest neighbors. This interpolation is done by taking a weighted average of the feature values of the original instance and the neighbor[5].

Benefits of SMOTE

Improves Model Performance: By balancing the class distribution, SMOTE helps machine learning models perform better on the minority class, leading to improved overall performance.

Reduces Overfitting: Unlike simple over-sampling that duplicates minority class instances, SMOTE creates new, diverse examples, which helps in reducing overfitting.

Widely Applicable: SMOTE can be applied to a variety of machine learning tasks, including classification problems in finance, healthcare, and more[6].

Limitations

Synthetic Data Quality: The quality of the synthetic data depends on the density and distribution of the minority class. Poorly generated samples can introduce noise.

Not Always Effective: In cases where the minority class is highly sparse or where the classes are not well separated, SMOTE may not significantly improve model performance[7].

In our project, we used the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of imbalanced data in the `Credit_Score` target variable. Originally, our data was imbalanced due to the fact that the 3 credit score class labels were not represented evenly. The "poor" class, represented by the number 0, had 11,139 instances, the "standard" class, represented by the number 1, had 18959 instances, and the "good" class, represent by the number 2, had 5593 instances. This is shown in the visual to the left. This imbalanced data can lead to biased models that perform poorly on the minority class. SMOTE generates synthetic samples for the minority class to ensure that all classes are represented equally, thus improving the performance and fairness of the model.

By using SMOTE, we balanced the dataset ensuring that each class in the Credit_Score target variable was equally represented. This step is crucial for training a fair and effective machine learning model.

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