cheatsheet

Scikit-learn Cheatsheet: Preprocessing

Preprocessing is the transformation of raw data into a format that is more suitable for machine learning algorithms.

What can be done?

• Scaling: Adjust the range or distribution of numeric features.
• Encoding: Convert categorical strings into numeric values.
• Transformation: Handle skewed data or non-linear relationships.
• Discretization: Bin continuous variables into intervals.

Key Tools

1. Scalers:
   • StandardScaler: $z = (x - \mu) / \sigma$. Standardizes to mean 0, variance 1.
   • MinMaxScaler: Scales to range [0, 1]. Sensitive to outliers.
   • RobustScaler: Uses median and interquartile range (IQR). Best for outlier-heavy data.
2. Encoders:
   • OneHotEncoder: Binary column per category.
   • OrdinalEncoder: Maps categories to integers [0, 1, 2…].
   • TargetEncoder: Encode categories based on the target mean (useful for high-cardinality).
3. Power Transforms:
   • PowerTransformer: Box-Cox or Yeo-Johnson transforms to make data more Gaussian-like.
   • QuantileTransformer: Maps data to a uniform or normal distribution.
4. Generating Features:
   • PolynomialFeatures: Creates $x^2, xy, y^2$. Captures interactions between features.

Tips

• Fit on Train, Transform on Test: Never fit a scaler on the whole dataset to avoid data leakage.
• Standardization: Required for distance-based models (KNN, SVM, K-Means) and Gradient Descent.

Code Snippet: Common Preprocessing

from sklearn.preprocessing import StandardScaler, OneHotEncoder, PolynomialFeatures
from sklearn.compose import ColumnTransformer

# 1. Scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# 2. Interaction Terms
poly = PolynomialFeatures(degree=2, interaction_only=True)
X_poly = poly.fit_transform(X)

# 3. Target Encoding (for categorical features)
from sklearn.preprocessing import TargetEncoder
encoder = TargetEncoder(smooth='auto')
X_encoded = encoder.fit_transform(X_cat, y)

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Credits: This cheatsheet is based on the scikit-learn documentation and examples, which are licensed under the BSD 3-Clause License. Copyright (c) 2007 - 2026 The scikit-learn developers. All rights reserved.

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