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python-3.xxgboostfeature-selectionhyperparametershyperopt

What should go first: automated xgboost model params tuning (Hyperopt) or features selection (boruta)

I classify clients by many little xgboost models created from different parts of dataset. Since it is hard to support many models manually, I decided to automate hyperparameters tuning via Hyperopt and features selection via Boruta.

Would you advise me please, what should go first: hyperparameters tuning or features selection? On the other hand, it does not matter. After features selection, the number of features decreases from 2500 to 100 (actually, I have 50 true features and 5 categorical features turned to 2 400 via OneHotEncoding).

If some code is needed, please, let me know. Thank you very much.

Solution

  • Feature selection (FS) can be considered as a preprocessing activity, wherein, the aim is to identify features having low bias and low variance [1].

    Meanwhile, the primary aim of hyperparameter optimization (HPO) is to automate hyper-parameter tuning process and make it possible for users to apply Machine Learning (ML) models to practical problems effectively [2]. Some important reasons for applying HPO techniques to ML models are as follows [3]:

    1. It reduces the human effort required, since many ML developers spend considerable time tuning the hyper-parameters, especially for large datasets or complex ML algorithms with a large number of hyper-parameters.

    2. It improves the performance of ML models. Many ML hyper-parameters have different optimums to achieve best performance in different datasets or problems.

    3. It makes the models and research more reproducible. Only when the same level of hyper-parameter tuning process is implemented can different ML algorithms be compared fairly; hence, using a same HPO method on different ML algorithms also helps to determine the most suitable ML model for a specific problem.

    Given the above difference between the two, I think FS should be first applied followed by HPO for a given algorithm.

    References

    [1] Tsai, C.F., Eberle, W. and Chu, C.Y., 2013. Genetic algorithms in feature and instance selection. Knowledge-Based Systems, 39, pp.240-247.

    [2] M. Kuhn, K. Johnson Applied Predictive Modeling Springer (2013) ISBN: 9781461468493.

    [3] F. Hutter, L. Kotthoff, J. Vanschoren (Eds.), Automatic Machine Learning: Methods, Systems, Challenges, 9783030053185, Springer (2019)