Why don’t we use the ID (e.g. student ID, social security number) as an input variable in a prediction problem?

Question

BlackTom AI · Accepted Answer

Question restatement: Why don’t we use the ID (e.g., student ID, social security number) as an input variable in a prediction problem?

Option provided: 'IDs are unique identifiers and do not contribute to patterns in data.'

Analysis of the option:
- Core idea: IDs are unique identifiers that should not carry predictive information about the target variable. In many predictive modeling scenarios, this is accurate because an ID is essentially a label for a record rather than a feature describing the data point.
- Why this is generally correct: Because IDs are by design unique to each instance, they do not encode a systematic relationship with the outcome that the model can learn from across instances. They tend to be arbitrary with respect to the target and typical feature relationships, so they usually don’t help the model generalize to new, unseen data.
- Important caveats and deeper reasoning:
  - Risks of leakage and overfitting: If IDs correlate with the target due to data collection practices (e.g., IDs encode information like enrollment year, class, or location), the model might latch onto spurious patterns tied to the IDs present in the training data. This harms generalization to new data where such IDs and their associated patterns may differ.
  - Generalization to unseen IDs: A model trained with IDs as features might fail to predict for IDs not seen during training, since the ID value itself provides no transferable information. This reduces predictive usefulness on future data.
  - Practical separation: IDs are often categorical but with high cardinality. Including them as raw features can create computational and memory challenges and can complicate model training without adding legitimate predictive value.
  - Exceptions exist: In rare cases, there might be a meaningful, privacy-preserving encoding of IDs (e.g., hashed group identifiers that proxy for a related attribute like region or school) that is handled with care to avoid leakage. Even then, the encoding must be treated as a feature with caution and proper validation.
- What this implies for practice: Generally, you should exclude IDs from input features unless you have a well-justified, privacy-conscious encoding strategy and rigorous validation demonstrating legitimate predictive benefit without leakage.
- Summary judgment: The statement captures a central principle in predictive modeling: IDs themselves do not encode meaningful patterns about the target and are not useful predictors in most scenarios, though be mindful of potential leakage and generalization issues if IDs correlate with the outcome in the data.

Why don’t we use the ID (e.g. student ID, social security number) as an input variable in a prediction problem?

查看解析

登录即可查看完整答案

类似问题

Which of the following is NOT an advantage of feature engineering?

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单