[FIXED] How to add training data to the model after initial training?

Issue

I am trying to add data for my scikit-learn model after it has already been trained. For example, I have the data that I used in the beginning (there are about 250 of them). After that, I need to train this model one more time by calling the function, and so on. The only thing that came to my mind was to add new values ​​to the existing data array every time and train the model again, but this is very resource-intensive and takes more time.

Is there another way to train the machine learning model?

model = LinearRegression().fit(test, result)
reg.predict(task)
### and here I want to add some data, for example one or two examples like:
model.addFit(one_test, one_result)

Solution

The short answer in your case (using the sklearn.linear_model.LinearRegression model) is no, it is not possible to add one or two more examples and train without adding this to the original training set and fitting it all at the same time. Under the hood, the model is simply using Ordinary Least Squares (described here) which requires the complete matrix of training data on which to fit your model. However, this algorithm is very fast and in the case of ~ hundreds of training examples, it would be very quick to re-calculate the parameters of the model with each new couple examples.

Answered By - TC Arlen