Identifying the most suitable model

The previous chapter showed you how to evaluate a model. The performance indices are useful to compare different models and/or parameters. Applying different techniques on the same data, we can compare a performance index to pick the most appropriate recommender. Since there are different evaluation metrics, there is no objective way to do it.

The starting point is the k-fold evaluation framework that we defined in the previous section. It is stored inside eval_sets.

Comparing models

In order to compare different models, we first need to define them. Each model is stored in a list with its name and parameters. The components of the list are as follows:

  • name: This is the model name.
  • param: This is a list with its ...

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