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I see suggestions like this for tuning a GradientBoostingRegressor's params through multiple steps. In each step, the best params are selected via CV. For example (abbreviated steps):

  1. find some of the best tree-based params through grid search
  2. find the remaining best tree-based params through grid search, having fixed other params in step 1
  3. decrease the learning rate and increase n_estimators

My question is whether this method of choosing the best hyperparams through multiple passes leads to overfitting or a cross-validation score that is overoptimistic in tree-based models like RandomForestRegressor or GradientBoostingRegressor.

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No, you don't over-fit

The method you chose to tune your parameters is independent of whether your performance estimation is biased (over-estimated) or not.

As long as you use CV for your parameter-tuning, you cannot "over-fit" in general (meaning to create a too complex model) but only be "over-precise" (like deciding whether using 319 or 318 trees is the best...) which, apart from consuming time, does not really harm you.

Edit: just to mention, if you do have small data-sets, really at the lower limit of what you feel comfortable, you can "overfit" to some extend. Simple tip: if you are using any boosting algorithms, don't go for the last 1 percent of the score; if you use any other algorithm, let the L2-regularization (or other, similar regularization) better be a little higher then the "optimum".

Beside: the site you mentioned has some good tips, mostly the one with the n_estimators/learning_rate ratio that can be increased in the end (only!).

(Remark: In very special cases, where you know that your training data is quite different from what you will predict on, pure CV-based optimization is not sufficient but there needs to be extended, problem specific tuning)

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  • $\begingroup$ In addition to the question of overfitting, I was also curious about evaluating model performance - it appears that the entire model building process would need to be repeated to actually estimate performance accurately, which is not computationally practical using this procedure $\endgroup$
    – Brian Bien
    Feb 1, 2017 at 15:56
  • $\begingroup$ What exactly do you mean by "model building process"? And what do you mean by "estimate performance"? Do you want to use a holdout set to estimate the performance? There are several ways to "estimate performance";) $\endgroup$
    – Mayou36
    Feb 2, 2017 at 10:38
  • $\begingroup$ Suppose you then use params tuned using this approach to then estimate performance of a GBR using K-fold CV. The hyperparams selected have been chosen using multiple passes through the dataset, in contrast to the more common K-fold CV pipeline that selects hyperparams inside each fold. $\endgroup$
    – Brian Bien
    Feb 5, 2017 at 16:15
  • $\begingroup$ There is the possibility to make a holdout set: you don't use all of the data for your cv-optimization but instead predict in the end on that holdout set (which is somehow a more reliable measure for how well your algorithm does as this set has not yet been involved into the hyper-optimization). If you have small data-sets in general, you can use this approach (as CV on the whole data can slightly over-fit). But you best read about the holdout-set strategy. And yes, I agree, if you want to do this K-folded as well, you have a huge computation time... $\endgroup$
    – Mayou36
    Feb 5, 2017 at 17:00

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