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Do you have to update model with new data?
You should not assume that simply optimizing the parameters on the old model (given new data) will produce a statistically significant improvement in your model, or a valid model for that matter. Such incremental updates should be done with proper model validation overseen by a data scientist.
When to re-train a model in machine learning?
There is no benefit to incrementally updating a model when the nature of the data and the system being modeled have not changed, but there are downsides: unnecessary extra work for the data scientist who must validate these new models. What are best-practices for re-training?
Is there a reason to re-train a model?
There is no reason to re-train if the error metric / KPI stays within your desired range (if your model is serving its purpose). There is no benefit to incrementally updating a model when the nature of the data and the system being modeled have not changed, but there are downsides:
How does a model adapt to the data?
With this method, your model learns in a sequential manner and sort of adapts locally to your data in that it will be more influenced by the recent observations than by older observations. This might be useful in situations where your model needs to dynamically adapt to new patterns in data.
How can I add new data to my pre-trained model?
How can I append some new data to my pre-trained model without retrain the model from the beginning. I cannot comment yet. If you just load the model and use a fit method it will update the weights, not reinstance all the weights. It will just perform a number of weights update that you can chose, using the new data.
How to update an SVM model with new data stack?
In sklearn you can do this only for linear kernel and using SGDClassifier (with appropiate selection of loss/penalty terms, loss should be hinge, and penalty L2). Incremental learning is supported through partial_fit methods, and this is not implemented for neither SVC nor LinearSVC.