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Building Recommendation Engines by Suresh Kumar Gorakala

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Evaluation techniques

In the previous sections, we saw various data mining techniques used in recommender systems. In this section, we will learn how to evaluate models built using data mining techniques. The ultimate goal for any data analytics model is to perform well on future data. This objective can be achieved only if we build a model, which is efficient and robust during the development stage.

While evaluating any model, the most important things we need to consider are as follows:

  • Whether the model is overfitting or underfitting
  • How well the model fits the future data or the test data

Underfitting, also known as bias, is a scenario in which the model doesn't even perform well on training data; this means that we are fitting a less robust model ...

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