Outlier Detection Cheat Sheet

Covers statistical and machine-learning methods for identifying outliers, including Z-score, IQR, Isolation Forest, and Local Outlier Factor, with runnable examples.

2 PagesIntermediateMar 5, 2026

Detection Methods

Common approaches ranked from simple to model-based.

Z-score- Flags points where |value - mean| / std exceeds a threshold (commonly 3); assumes roughly normal data
IQR method- Flags points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR; robust to non-normal distributions
Isolation Forest- Isolates points by random recursive splitting; anomalies need fewer splits to isolate
Local Outlier Factor (LOF)- Compares local density of a point to its neighbors' density; good for varying-density clusters
Elliptic Envelope- Fits a robust Gaussian ellipse to the data; points outside it are outliers (assumes elliptical data)
DBSCAN- Density-based clustering where points not assigned to any cluster are treated as outliers

IQR Method in Pandas

Flag outliers in a single column using the interquartile range.

python

Q1 = df['value'].quantile(0.25)Q3 = df['value'].quantile(0.75)IQR = Q3 - Q1lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQRoutliers = df[(df['value'] < lower) | (df['value'] > upper)]print(f"Found {len(outliers)} outliers outside [{lower:.2f}, {upper:.2f}]")

Isolation Forest

Unsupervised outlier detection for multivariate data.

python

from sklearn.ensemble import IsolationForestclf = IsolationForest(n_estimators=100, contamination=0.05, random_state=42)clf.fit(X)# -1 = outlier, 1 = inlierlabels = clf.predict(X)scores = clf.decision_function(X)  # Higher = more normal

Local Outlier Factor

Detect outliers based on local density deviation.

python

from sklearn.neighbors import LocalOutlierFactorlof = LocalOutlierFactor(n_neighbors=20, contamination=0.05)labels = lof.fit_predict(X)            # -1 = outlier, 1 = inlierscores = lof.negative_outlier_factor_  # More negative = more anomalous

Pro Tip

Z-score and the elliptic envelope both assume roughly Gaussian, single-cluster data -- with skewed distributions or multiple clusters, prefer IQR, Isolation Forest, or LOF instead.

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