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Generally speaking, we all know it's to save spaces with incremental learning.

According to the ques in stackoverflow , it also said that.

But what's the disadvantages?

What I know from my experiments is two points below:

  1. Train with subsets of data but shouldn't be too small. I prepared very small datasets and the predict result is very worse.

  2. When training for a very long time, some elder behavors will be forgotten due to the multiple training epochs.

That's all from my experience when training with xgboost incrementally.

Or anything else?

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  • $\begingroup$ An epoch general refers to as one full pass through the training data. So the training set is repeated each epoch. Each epoch consists of many batches, usually. $\endgroup$ – jonnor Apr 18 at 0:11
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It is more complicated. Instead of just having all the entire dataset in memory and doing performing a single fit, there are now more things to do:

  • Need a way to load data in batches from disk
  • Need to decide a new hyperparameter, the batchsize
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