Model issue type refers to the categories of problems that may arise when developing or using machine learning models. These can include overfitting, underfitting, bias, variance, and poor generalization performance. Understanding the specific type of model issue can help to identify root causes and take steps to improve model performance and accuracy.
Examples of model issue types include:
- Data issues (anomalies, or stability problems)
- Model precision (issues with accuracy, sensitivity, AUC, …)
- Model stability (e.g. overfitting)
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