Python Outlier Detection library, covers many algorithms (tabular, numeric).
| PyOD - Ensembles | Combines multiple detectors — if a point scores high across methods, it is more confidently an outlier. |
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# Step 1: Import libraries
from pyod.models.iforest import IForest # Isolation Forest from PyOD
from pyod.utils.data import generate_data
from sklearn.model_selection import train_test_split
# Step 2: Generate synthetic data (normal + outliers)
X, y = generate_data(n_train=200, n_test=100, n_features=2, contamination=0.1)
# Step 3: Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Step 4: Initialize detector
clf = IForest(contamination=0.1) # 'contamination' = expected proportion of outliers
# Step 5: Train model
clf.fit(X_train)
# Step 6: Predict anomalies
y_train_pred = clf.labels_ # 0 = normal, 1 = outlier
y_train_scores = clf.decision_scores_ # raw outlier scores
y_test_pred = clf.predict(X_test) # predict on unseen test set
y_test_scores = clf.decision_function(X_test) # outlier scores for test
# Step 7: Evaluate (basic accuracy example)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_test_pred))