Evaluating data-mining algorithms
In the previous sections, we have seen various data-mining techniques used in recommender systems. In this section, you 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 could be achieved only if we build a model that 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 over fitting or under fitting
How well the model fits the future data or test data
Under fitting, also known as bias, is a scenario when the model doesn't even perform well on training data. This means that we fit a less robust model to the data. For example, say the data is distributed non-linearly and we are fitting the data with a linear model. From the following image, we see that data is non-linearly distributed. Assume that we have fitted a linear model (orange...
