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Encoding categorical variables without creating leakage

Dr. Elena Vasquez Apr 2026

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import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

numeric_features = ['age', 'income', 'tenure_months']
categorical_features = ['country', 'plan_tier', 'device_type']

numeric_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler()),
])

categorical_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False)),
])

preprocessor = ColumnTransformer([
    ('num', numeric_pipeline, numeric_features),
    ('cat', categorical_pipeline, categorical_features),
])

print(preprocessor)

1 file · python Explain with highlit

Categoricals are where good intentions become leakage. I use one-hot encoding for low-cardinality stable fields, ordinal encoders only when order is real, and frequency or target encoders with strict cross-validation boundaries. The encoder strategy should reflect both statistical behavior and production serving constraints.

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