What do predictive models rely on to select useful inputs?

Predictive models rely on input variable selection to identify the most relevant variables that contribute meaningfully to the prediction outcome. This process is vital because not all available features in a dataset hold significant predictive power. By focusing on the most useful inputs, a model can increase its accuracy and reduce overfitting, which can occur when too many irrelevant features are included.

In the context of predictive analytics, input variable selection includes various methods for evaluating and choosing which variables to include in the model. This practice can involve statistical tests, algorithms, or machine learning techniques that assess the importance of each input in relation to the target variable. A well-executed selection process leads to simpler models that are not only easier to interpret but also more robust.

The other concepts, while related to model performance, do not directly pertain to the process of selecting which inputs to use. Generalization techniques focus on ensuring that a model performs well on new, unseen data, whereas efficiency models pertain more to optimizing resource usage or time. Complexity measures analyze the complexity of a model rather than assisting in the selection of input variables.