Does model get retrained entirely using .fit() in sklearn and tensorflow
In sklearn, the .fit() method retrains on the dataset i.e as you use .fit() on any dataset, any info pertaining to previous training will all be discarded. So assuming you have new data coming in every day you will have to retrain each time in the case of most sklearn algorithms.
Although, If you like to retrain the sklearn models instead of training from scratch, some algorithms of sklearn (like SGDClassifier) provide a method called partial_fit(). These can be used to retrain and update the weights of an existing model.
As per Tensorflow, the .fit() method actually trains the model without discarding any info pertaining to previous trainings. Hence each time .fit() is used via TF it will actually retrain the model.
Tip: you can use SaveModel from TF to save the best model and reload and re-train the model as and when more data keeps flowing in.
I am trying to use machine learning in Python. Right now I am using sklearn and TensorFlow. I was wondering what to do if I have a model that needs updating when new data comes. For example, I have financial data. I built an LSTM model with TensorFlow and trained it. But new data comes in every day, and I don't want to retrain the model every day. Is there a way just to update the model and not retrain it from scratch?
In sklearn, the documentation for .fit() method (using DecisionTreeClassifier as an example) says that it

Build a decision tree classifier from the training set (X, y).

So it seems like it will retrain the entire model from scratch.
In tensorflow, .fit() method (using Sequential as an example) say

Trains the model for a fixed number of epochs (iterations on a
dataset).

So it seems like it does update the model instead of retraining. But I am not sure if my understanding is correct. I would be grateful for some clarification. And if sklearn indeed retrains the entire model using .fit(), is there a function that would just update the model instead of retraining from scratch?