Automated Machine Learning in Action by Qingquan Song Haifeng Jin Xia Hu

Author:Qingquan Song, Haifeng Jin, Xia Hu [Qingquan Song, Haifeng Jin, Xia Hu]
Language: eng
Format: epub, mobi
Publisher: Manning Publications Co.
Published: 2022-04-20T22:00:00+00:00

Figure 6.3 The reconstructed images from the tuned autoencoder

6.3 Tuning shallow models with different search methods

In chapter 2, you learned how to tune a traditional (or shallow) ML model with grid search. Appendix B also covers several more complex examples of tuning multiple components in an ML pipeline. All of the examples leverage the built-in tuner (GridSearchCV) in the scikit-learn library. Now that you’ve seen the power of KerasTuner for selecting and tuning deep learning models, you may be wondering whether you can also tune shallow models with KerasTuner. Compared to tuning using GridSearchCV, doing this with KerasTuner has the following two key benefits:

It gives you a more straightforward way to perform model selection without tuning each model separately and comparing them manually. With the help of the conditional scope defined by the hyperparameter class, you can select between different shallow models the same way you did with deep learning models. We’ll introduce how to do this in the next example.

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