Question one:

Now I have more than 400,000 pieces of data, and I need to use some kind of machine learning classification algorithm to build a model for this data. The problem I encountered is that the data is too large and cannot be read at once, so Want to ask how to process data?

Question 2:

I have a question about sklearn cross-validation: If I have 10,000 training data, these 10,000 training data sets can be divided into n groups of training using the KFold method based on the cross-validation principle (train data accounts for 0.7). Now What I don’t understand is that I fit() the training set of the first group, and then performed prediction verification on the test set to get the prediction accuracy. But what is the use of getting the prediction accuracy? Will it affect the next training session? Also, will the last trained model be used in the next fit() function?