Suppose a machine learning model is trained on two features (f1, f2). If there comes a new feature(f3) is there any way to pass this new feature to existing model and retrain instead of training a model again on the three features?
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So the question asks if you can retrain the model after adding another feature.
Answer is yes but you need to instantiate a new model which can accept a 3-feature/dimensional input.
This is due to the mechanics of neural networks whereby in the 1st layer, the features in your original model would be multiplied by a ($2 x n$) matrix where $n$ represents the number of hidden neurons in the second layer of the model. Therefore by multiplying a ($3 x 1$) input with the weight matrix will throw an error.
