监督模型训练与测试数据归一化相关技术问题咨询

Let’s tackle your two questions one by one—normalization is a critical step in many ML workflows, so it’s great you’re digging into the details!

1. Do test samples for supervised models need normalization? #

The short answer: It depends on the model, but if you normalized your training data, you must normalize the test data using the exact same statistics (mean, standard deviation, min/max values) calculated from the training set.

Here’s a breakdown by model type:

• Models that require normalization: Any model relying on distance calculations (like k-NN, SVM) or linear operations (like linear regression, logistic regression, neural networks) will be heavily impacted by feature scale differences. For these, normalization is non-negotiable for both training and test data—but never use test set statistics to normalize the test data.
• Models that don’t require normalization: Tree-based models (decision trees, random forests, XGBoost) make splits based on feature thresholds, not absolute values or distances. Feature scales don’t affect their performance, so you can skip normalization entirely for these.

2. Clarifications on training vs. test data normalization (with k-NN focus) #

You’re absolutely right about k-NN: since it measures similarity via distances (e.g., Euclidean distance), features with larger scales will dominate the similarity calculation. For example, if you have a feature like "annual income" (ranging from $20k to $100k) and "age" (ranging from 0 to 100), the income feature’s differences will completely overshadow age differences without normalization.

The non-negotiable rule here is:

Normalization (and any preprocessing step) should always be fit on the training set only, then applied to both training and test sets.

Why? Because in real-world scenarios, you won’t have access to test data when training your model. If you calculate normalization stats using the test set, you’re leaking information about the test distribution into your training process—this leads to overly optimistic performance estimates and a model that won’t generalize well to unseen data.

For example, if you use the test set’s mean to standardize the test data, you’re effectively using information your model wouldn’t have in production, making your validation results unreliable.

Quick k-NN workflow example: #

1. Split your data into training and test sets first.
2. Calculate mean/standard deviation (for Z-score normalization) or min/max values (for min-max normalization) from the training set only.
3. Apply these values to normalize both the training and test sets.
4. Train your k-NN model on the normalized training set.
5. Evaluate the model on the normalized test set.

内容的提问来源于stack exchange，提问作者Doppler