If a feature has the same value for all records, then:

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BlackTom AI · Accepted Answer

Exploring what happens when a feature has the same value for all records requires careful evaluation of each option.
Option 1: 'it causes overfitting.' This is unlikely because a constant feature provides no new information to the model and cannot help the model generalize better; overfitting is typically driven by overly complex models or noisy features, not by a feature that is identical everywhere.
Option 2: 'it must be one hot encoded.' One-hot encoding is used for categorical features with multiple distinct categories. A perfectly constant feature does not have multiple categories to distinguish, so encoding it would be unnecessary and may even introduce redundant columns without adding information.
Option 3: 'it contributes no information and should be removed.' This is correct because a constant feature does not vary across samples and therefore cannot improve predictions; removing it simplifies the model without sacrificing performance.
Option 4: 'it helps reduce variance.' A constant feature cannot influence variance reduction in a model; variance reduction typically comes from learning from informative features and regularization, not from non-informative constants.
In summary, the central idea is that a feature with identical values across all records adds no discriminatory power, so pruning it is the appropriate action.

If a feature has the same value for all records, then:

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