I’m working on a machine learning project to predict customer churn, but my model keeps underperforming. I’ve noticed that there are significantly more records of customers who didn’t churn compared to those who did. Could this discrepancy be causing my model’s issues? How do I handle an imbalanced data set to improve its performance? Any advice or best practices would be greatly appreciated!
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Yes,… the imbalance in your dataset is likely contributing to your model’s underperformance. When one class significantly outnumbers the other, the model can become biased towards the majority class, leading to poor performance in predicting the minority class (in this case, customers who churn).
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Adjust the decision threshold to improve your desired metric.
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When there is unequal representation of the classes in the dataset, it is said to be unbalanced. This is typical of classification difficulties in which there may be a class that is noticeably more common than others. For instance, there may be significantly more non-fraudulent transactions than fraudulent ones in a dataset used for fraud detection. A balanced dataset has roughly equal numbers of positive and negative labels. The dataset is unbalanced, though, if one label is more prevalent than the other. In an imbalanced dataset, the less common label is referred to as the minority class, and the prevalent label is called the majority class.