Automatic model generation

Model generation and hyperparameter tuning is the typical third macro-step of a machine learning pipeline (see Figure 13.1).

Model generation consists of creating a suitable model for solving specific tasks. For instance, you will probably use CNNs for visual recognition, and you will use RNNs for either time series analysis or for sequences. Of course, many variants are possible, each of which is manually crafted through a process of trial and error and works for very specific domains.

Hyperparameter tuning happens once the model is manually crafted. This process is generally very computationally expensive and can significantly change the quality of the results in a positive way. That’s because tuning the hyperparameters can help to optimize our model further.

Automatic model generation is the ultimate goal of any AutoML pipeline. How can this be achieved? One approach consists of generating the model by combining a set of primitive operations...