当无需模型在新数据上的无偏准确率估计时，是否仍需设置Test Set？

Great question—let’s break this down based on your specific context, since there’s no one-size-fits-all answer here.

First, let’s confirm you’ve got the core roles nailed (you already do, but it helps ground the discussion):

• Training Set: Teaches the model patterns in the data
• Validation Set: Compares different models or tunes hyperparameters for the same model
• Test Set: Delivers an unbiased estimate of how the model performs on unseen, real-world data

Now, to your question: You don’t strictly need a Test Set if your current scenario truly has no use for an unbiased generalization estimate—here’s when that makes perfect sense:

• You’re only doing quick prototyping or exploring a specific idea (e.g., testing if a new feature moves the needle on your existing data)
• The model will never encounter data outside your current dataset (e.g., a one-off internal analysis where you’re only working with the data you already have)
• Your dataset is very small, and splitting off a Test Set would leave too little data for training/validation, directly hurting model performance

That said, there’s a key caveat worth considering: even if you don’t need the unbiased estimate now, it’s often wise to reserve a Test Set if you have enough data. Why?

• If you later decide to iterate on the model, or a stakeholder suddenly asks for proof of its real-world performance, you’ll already have a clean, untouched dataset to use. Without it, you’d risk data leakage if you split your existing training/validation data later (since you’ve already used that data to tune hyperparameters, any new split would no longer be "unseen").

If your dataset is too small for a three-way split, an alternative is to use k-fold cross-validation instead of a separate Validation Set. This lets you use most of your data for training while still getting reliable estimates of model performance—no Test Set needed in this case either.

Bottom Line #

Skip the Test Set only if:

1. You have no current or future need for an unbiased generalization estimate
2. Splitting off a Test Set would harm your model’s training (due to limited data size)

Otherwise, reserving a small Test Set (even if you don’t use it immediately) is a safe, forward-thinking choice.

内容的提问来源于stack exchange，提问作者user2777745