6 · Splitting, Validation & Experiment Design · Data Leakage & Experimental Integrity

Preprocessing leakage

Scaling, imputation, PCA or feature selection fitted on the full dataset exposes validation/test distribution information. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.

Reference lessonPython exampleVisual explanation

Intuition first

What this concept means in practice

The practical value of Preprocessing leakage comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.

A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.

PurposeUse split-aware pipelines to prevent it.

MechanismA transformation estimates information using data that later acts as validation/test data.

EvidenceInspect intermediate and final output; compare with an independent expectation.

Main cautionEven unsupervised transforms such as PCA can leak distribution information if fitted globally.

Mechanism

Trace the operation from input to decision

A transformation estimates information using data that later acts as validation/test data.

1Input→

2Apply rule→

3Inspect state→

4Validate→

5Use result

Key rule

fit transforms on train only

Visual explanation

Make the structure visible

The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.

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Practical example

Where you would use it

Standardising the entire dataset before CV lets validation statistics affect training-fold scaling.

Use when

Use split-aware pipelines to prevent it.

Pitfall

What can make the result misleading

Watch out

Even unsupervised transforms such as PCA can leak distribution information if fitted globally.

A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.

Implementation

Miniature Python example

Keep the example small enough that you can inspect each stage manually.

Python

# Purpose: demonstrate Preprocessing leakage with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Import the library or helper used in this example.
# Step 2 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score

# Create the numerical values used in the calculation.
# Step 3 — Construct `y_true` as an array so vectorised numerical operations can be applied consistently.
y_true = np.array([0,0,0,0,0,1,1,1,1,1,1,1])
# Step 4 — Construct `p` as an array so vectorised numerical operations can be applied consistently.
p = np.array([.08,.12,.22,.48,.56,.31,.58,.64,.70,.76,.82,.91])
# Store this intermediate value with a descriptive name for the next step.
# Step 5 — Execute this statement and inspect how it changes the current value, object or program state.
y_pred = (p >= .50).astype(int)
# Print this intermediate result so you can verify the workflow step by step.
# Step 6 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Threshold: 0.50")
# Print this intermediate result so you can verify the workflow step by step.
# Step 7 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Confusion matrix:\n", confusion_matrix(y_true,y_pred))
# Print this intermediate result so you can verify the workflow step by step.
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Accuracy:", round(accuracy_score(y_true,y_pred),3))
# Print this intermediate result so you can verify the workflow step by step.
# Step 9 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Precision:", round(precision_score(y_true,y_pred),3))
# Print this intermediate result so you can verify the workflow step by step.
# Step 10 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Recall:", round(recall_score(y_true,y_pred),3))
# Print this intermediate result so you can verify the workflow step by step.
# Step 11 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · F1:", round(f1_score(y_true,y_pred),3))
# Print this intermediate result so you can verify the workflow step by step.
# Step 12 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · ROC-AUC:", round(roc_auc_score(y_true,p),3))

Expected / illustrative output

STEP 1 · Threshold: 0.50
STEP 2 · Confusion matrix:
 [[4 1]
 [1 6]]
STEP 3 · Accuracy: 0.833
STEP 3 · Precision: 0.857
STEP 3 · Recall: 0.857
STEP 3 · F1: 0.857
STEP 3 · ROC-AUC: 0.943

Implementation checklist

Before you move on

Can you state what data or object enters the operation?
Can you explain what changes and what must remain invariant?
Have you checked the result on a tiny case you can verify independently?
Have you considered the main failure mode described above?
Can the operation be reproduced from code/formulas and documented assumptions?