cheatsheet

Scikit-learn Cheatsheet: Impute (Handling Missing Data)

The impute module provides strategies to handle missing values (NaN) in datasets.

What can be done?

• Univariate Imputation: Fill missing values using a single feature’s statistics.
• Multivariate Imputation: Predict missing values using all other available features (more accurate).
• Nearest Neighbor Imputation: Fill based on similar samples.
• Marking Missingness: Add a binary indicator feature for where data was missing.

Key Algorithms

1. SimpleImputer:
   • strategy='mean', 'median', 'most_frequent', or 'constant'.
   • Fast and simple baseline.
2. IterativeImputer:
   • Models each feature with missing values as a function of others in a round-robin fashion.
   • Inspired by R’s MICE (Multivariate Imputation by Chained Equations).
3. KNNImputer:
   • Finds $K$ nearest neighbors for each sample with a missing value and averages their values for that feature.
4. MissingIndicator:
   • Useful when the fact that a value is missing is informative.

Tips

• Always fit on training data and transform on test data.
• If using IterativeImputer, you must enable it first as it is experimental: from sklearn.experimental import enable_iterative_imputer.

Code Snippet: Advanced Imputation Pipeline

import numpy as np
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer

X = [[1, 2], [np.nan, 3], [7, 6], [4, np.nan]]

# 1. Simple Mean Imputation
imp_mean = SimpleImputer(strategy='mean')
X_simple = imp_mean.fit_transform(X)

# 2. KNN Imputation (Weights by distance)
imp_knn = KNNImputer(n_neighbors=2, weights="distance")
X_knn = imp_knn.fit_transform(X)

# 3. Iterative Imputation
imp_iter = IterativeImputer(max_iter=10, random_state=0)
X_iter = imp_iter.fit_transform(X)

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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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