# How does the number of trees effect the prediction time in gradient boost classification trees?

After tuning hyper-parameters for a gradient boosted model, I have found that the best tree count (iterations) is a few thousand. I'm worried that such a high count might impact prediction performance. Can someone explain the relation between trees count and prediction time?

You'll need to test it yourself and check if it's fast enough for your use case. Modern libraries like xgboost can handle high numbers of trees with remarkable efficiency. One thing to keep in mind is that it's generally faster to get predictions for a whole block of test examples at once than it is to get the predictions one at a time.