Consequences of Using Non-Informative Predictors

Using non-informative predictors can lead to selection bias and decrease model performance, making results unreliable. The presence of irrelevant features may also increase computational complexity without improving accuracy.

Approaches for Reducing the Number of Predictors

  1. Wrapper Methods: These methods evaluate subsets of predictors by training models and selecting those with the best performance.
  2. Filter Methods: In contrast, these rely on statistical measures to rank features before applying a model.

Selection Bias

This occurs when certain predictors are favored over others, often due to data issues or misuse of feature selection methods.

Misuse of Feature Selection

Common pitfalls include using too few or too many predictors, which can cause overfitting or underfitting in models.

Case Study: Predicting Cognitive Impairment

In a study focused on predicting cognitive impairment, improper feature selection led to misleading conclusions. By using both filter and wrapper methods, the case study demonstrated how combining techniques can improve prediction accuracy.

Computing & Exercises

Practical applications of these concepts often include computation-heavy processes, and exercises typically involve hands-on work with real datasets to reinforce learning.