Table of Contents
1. Introduction: When ML Models Fail
- Poor Data Quality
- Overfitting
- Inadequate Model Evaluation
- Data Drift
Imagine you have built a machine learning model by training the data, tested it, and even pushed it into production. But you realize it is not working as expected, such as providing wrong predictions, not being updated with new data, underperforming, and not improving with time. Model failures are common; even the best models stumble at times. There can be several common reasons why this happens. Through this blog we will understand 4 common reasons why ML models fail.
- Poor data Quality
Data is the foundation for any AI and ML model. If the data is of poor quality, the model will tend to fail. The poor-quality data is considered to have missing values, inconsistent datasets, imbalanced data sets etc. For example, in a dataset, if age is missing, it leads to incompleteness, and the model might not predict accurately. It is important for businesses to implement strong techniques like imputation, normalization, and data augmentation. Implementing data validation pipelines to detect and correct inconsistencies is another important way to detect the quality of data. - Overfitting data
Overfitting in machine learning means the training data is learned too well from the noise and the relevant data both, resulting in poor performance in new and unseen data. In this case, the model memorizes the training data rather than the underlying patterns. This leads to more accuracy on the training set but low accuracy on validation. Additionally, the model fails to generalize to new and diverse datasets. A way to reduce the overfitting of data is through, applying regularization techniques (like L1/L2) and reducing model complexity to prevent memorizing noise. - Inadequate model evaluation
Poor evaluation of models provides inflated estimates of how well a model performs. This could be because of using completely wrong metrics, like using accuracy instead of F1-score on unbalanced datasets, or allowing data leakage in a training process, or not using the right validation methods (like k-fold cross-validation), all of which will yield biased estimates of the results. For example, a spam detection model might show 95% accuracy simply because most emails aren’t spam, but its F1 score could be poor if it fails to catch actual spam messages. Appropriate evaluation metrics like the F-1 score prevent data leakage, and validation methods like k-fold cross-validation provide reliable model performance estimates. - Data drift
Data drift means how the input value changes, even though the model does not change. For example, if we have built a recommendation model in 2020 that suggests clothes to customers with then fashion like skinny jeans, which is probably changed in 2025 with baggy jeans. But the model still suggests products like skinny jeans to people because it has learned from the old 2020 data. So, from the technical point of view, the model will suggest old information and would not be up to date.As a solution, a continuous data monitoring solution needs to be implemented, as well as model retraining pipelines as well. It is important to regularly compare incoming live data distributions with training data to detect drift using statistical tests like, KL divergence, PSI etc.
Conclusion
Machine learning models often encounter several common challenges. By recognizing and tackling challenges such as low data quality, overfitting, inappropriate evaluation techniques, and data drift, we can develop models that deliver improved performance and maintain their reliability in the long run.
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Prescience Team
