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joseph1
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Begginer
Asked: 2025-05-29T21:46:38+00:00 In:

What's the best way to normalize data without leaking info from the test set into the training process?

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I used StandardScaler on the entire dataset before splitting, and later realized that might be wrong. How should I handle scaling correctly to avoid leakage?

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2 Answers

  1. Charlesg
    Begginer

    When I first started working with machine learning, I made this classic mistake: I normalized my entire dataset before splitting it. And guess what? My model performed great a little too great. 😅
    Turns out, I was leaking information from the test set into training without even realizing it.

    Here’s what I do now (and always recommend):

    1. First, split your data into train and test (or train/val/test).
    2. Fit your scaler only on the training set — not the whole dataset.

      python
      scaler = StandardScaler()
      scaler.fit(X_train)
    3. Then use that same scaler to transform both the training and test sets.

      python
      X_train_scaled = scaler.transform(X_train)
      X_test_scaled = scaler.transform(X_test)

    That way, your model learns only from the training data — just like it would in a real-world setting. No sneak peeks at the test set.
    Trust me, once you catch this, you’ll never scale data the old way again. It’s a small thing, but it makes a huge difference in keeping your model honest.

  2. Hassaan Arif
    Enlightened

    To normalize data without leaking test set information, always follow this golden rule: compute normalization parameters only on the training data.

    Here’s the correct process:

    Split your data first – before any preprocessing.

    Fit the scaler only on training data – e.g.,

    scaler.fit(X_train).

    Transform both sets using that scaler –

    scaler.transform(X_train) and scaler.transform(X_test).

    This ensures your model only learns from what it truly should know, preserving the integrity of your evaluation. It’s a small step with a huge impact think of it as respecting the boundary between practice and the real test.