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When to retrain a machine learning model
Tips and techniques to decide whether it is time to retrain our model
Training a model is a complex process requiring much effort and analysis. Once a model is ready, we know that it won’t be valid forever and that we’ll need to train it again. How can we decide if a model needs to be retrained? There are some techniques that help us.
The need for retraining
Data changes every day. Big Data changes every minute. Every model we could ever create will necessarily learn from the past. If our data statistics in the future don’t change, our model will likely remain stable and efficient. But if it changes, the model may become obsolete and need to be retrained.
It’s not only an issue related to how data changes, but it’s related to how the distribution of our data changes. Even the correlation between the features may introduce some new stuff that the model may not be able to handle properly.
Think about the introduction of a new product, a marketing push that produces an increase in sales, or even worse, a change in some search engine algorithms that lowers the organic traffic dramatically. All these examples are exogenous events that change the distribution of our data and may potentially affect the performance of…