I have a machine learning model that is getting worse each day. How do I investigate the causes? Do I look at the classifications and validations that have been done before? Is there anything else I need to look at?
Here are steps to help you diagnose and address the issue:
Check Data Quality and Distribution:
Ensure that the data your model is trained on hasn’t changed significantly. Data quality issues, such as missing values, outliers, or noisy data, can affect model performance.
Data Drift Analysis:
Monitor for data drift, where the distribution of your input data (features) changes over time. This can impact the model’s performance. Use statistical tests and visualization techniques to detect drift.
Well there could be many possible reason behind
I will try to point out some of them that can be validated on priority
Let’s go one by one
Classifications and validations:
As you said it is important to track the model’s performance on both training and validation data over time. If the model’s performance on the validation data is decreasing, this is a sign that the model is overfitting to the training data.
It is equally important to know if your model is being exposed to new data that is different from the data that it was trained on, this could also lead to a decrease in performance.
This is something I have faced which ended in retraining the model. So better have a view on it
Changes to the model:
If you have made any changes to the model, such as changing the hyperparameters or the algorithm, this could also affect the model’s performance.
Changes to the environment and format:
If the environment in which the model is being used has changed, such as if the data format has changed or if the hardware has changed, this could also affect the model’s performance.