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This is a quick question. If I compare neural network and random forest, the data size requirement is huge in neural network, but a decision tree or random forest can work with less number of records too.

Does any such problem occurs with XGBoost as well? Does it also need a lot of data so that it can go in multiple iterations to reduce the error term?

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The amount of data you need depends on the problem (see this great article on learning curves), but in general xgboost is very data efficient like random forests and has found a lot of use where data is expensive to produce as in medicine. Try it out on your data and plot a learning curve - if it is under-fitting, you need more data.

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No. Xgboost is more like traditional ML algorithm. It doesn't need too much data and it also perform better than almost all ML algorithm

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