How does data imbalance affect a machine learning model?

Data imbalance impacts a machine learning model significantly by causing biased predictions that favor the majority class. When the dataset contains a disproportionate number of instances for different classes, the model tends to learn the patterns associated with the majority class better than those of the minority class. As a result, when making predictions, the model is likely to predict the majority class more often, leading to a skewed understanding of the data.

For example, in a scenario where 95% of the data points belong to the positive class and only 5% belong to the negative class, the model might achieve a high overall accuracy simply by predicting "positive" for all instances. However, this would not reflect its performance on the minority class, which may be of greater interest in some applications, such as fraud detection or medical diagnosis.

Addressing data imbalance often involves strategies like resampling, using different evaluation metrics (like precision, recall, or F1-score), or applying specialized algorithms designed to rectify biases, demonstrating its critical impact on the effectiveness of a machine learning model.