训练集与验证集的区别及全数据训练再验证的合理性疑问

Great question—this is one of the most fundamental concepts in machine learning, and it’s totally normal to scratch your head over it at first. Let’s break this down clearly.

Why You Can’t Validate on the Same Data You Trained On #

Imagine you’re studying for a math exam. If you only memorize the exact problems from your practice workbook, you’ll ace the practice test—but if the real exam has new problems you haven’t seen, you’ll bomb it. That’s exactly what happens with ML models when you validate on training data:

• The model doesn’t learn general patterns from the data—it learns to memorize every detail, noise, and outlier in the training set. This is called overfitting.
• When you test on the same data, you’ll get a misleadingly high accuracy score (or low loss) because the model is just regurgitating what it already saw. But when you deploy it to real-world data it hasn’t encountered before, it’ll perform terribly.
• Validation sets exist to act as a "neutral judge"—they’re data the model hasn’t seen during training, so they give you a realistic idea of how well it will generalize to new inputs.

Key Differences Between Training and Validation Sets #

Let’s break down their roles and characteristics:

Training Set #

• Purpose: This is the data the model uses to learn. It’s where the model adjusts its internal weights and parameters to fit the patterns in the data. Think of it as the "textbook" or "lecture material" for the model.
• Size: Typically makes up 70-80% of your total dataset (the exact split depends on how much data you have).
• Interaction: The model sees every sample in the training set multiple times during training epochs, and its parameters are updated directly based on this data.

Validation Set #

• Purpose: This data is used to evaluate the model’s performance during training (before final testing). It helps you tweak hyperparameters (like learning rate, model depth, or regularization strength) and catch overfitting early. Think of it as a "practice exam" that doesn’t count towards the final grade but helps you adjust your study strategy.
• Size: Usually 10-15% of your total dataset.
• Interaction: The model never learns from this data—its parameters aren’t updated based on validation set performance. It’s only used to measure how well the model is generalizing beyond its training material.

A quick side note: Sometimes people also use a test set (the remaining 10-15%) for a final, unbiased evaluation after you’ve finalized your model. But validation sets are critical during the iterative training process to avoid overfitting to the test set too!

内容的提问来源于stack exchange，提问作者Shoop