See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html and sklearn/base.py for more information.

Return sklearn-style Tags for compatibility with scikit-learn >= 1.8. We mark all PyOD detectors as 'outlier_detector' so that utilities

Internal Function to Calculate Average Absolute Deviation (a.k.a Mean Absolute Deviation)

Add a NEW field to nodes_arr to simulate a future sklearn that introduces a field PyOD has not yet allowlisted.

Module-level constructor used by ``_OldDtypeTree.__reduce__``. Must be importable so pickle can resolve it on load.

Dispatch for `pyod info`. Self-diagnostic; returns 0 in core installs.

Dispatch for `pyod install skill`. Delegates to the shared helper.

Dispatch for `pyod mcp serve`. Delegates to `pyod.mcp_server.main`.

Deterministic mock encoder for testing.

Private method to calculate ecdf in parallel. Parameters ---------- n_dims : int The number of dimensions of the current input

Private method to calculate ecdf in parallel. Parameters ---------- n_dims : int The number of dimensions of the current input mat

Internal function to calculate key attributes: - threshold_: used to decide the binary label - labels_: binary labels of training dat

Render a deduplicated list of benchmark refs cited in the KB.

Render a one-line summary of total detector counts by modality.

Analyze a detection result. Wraps ``ADEngine.analyze_results``. Pass the JSON returned by ``run_detection`` as ``result``; optionally include

Get constructor metadata for a detector from a plan. Returns import path, class name, params, and a Python code snippet for instantiation. Pa

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. `coef_`

Compare detectors for a given data type. Args: names: Comma-separated detector names. If empty, top-k for type. data_type: Data t

True when convergence was reached in fit(), False otherwise. Decorator for scikit-learn Gaussian Mixture Model attributes.

Estimated robust covariance matrix. Decorator for scikit-learn MinCovDet attributes.

The covariance of each mixture component. Decorator for scikit-learn Gaussian Mixture Model attributes.

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. For new data, anomaly scores are approximated by the weighted average of th

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Predict raw anomaly score of X using the fitted detectors. The anomaly score of an input sample is computed based on different detect

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detector

Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detecto

Coefficients of the support vectors in the decision function. Decorator for scikit-learn One class SVM attributes.

Calculated the empirical CDF of a given dataset using the statsmodels function. Parameters ---------- X : numpy ar

Encode text or images to embeddings. Parameters ---------- X : list of str (text) or list of PIL.Image (image) In

The collection of fitted sub-estimators. Decorator for scikit-learn Isolation Forest attributes.

The indeces of the subset of features used to train the estimators. Decorator for scikit-learn Isolation Forest attributes.

The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Decorator for scikit-learn Isolation Forest attributes

Explain a PyOD detector: how it works, strengths, weaknesses, benchmark performance, and recommended use cases.

Explain why specific samples were flagged as anomalies. Wraps ``ADEngine.explain_findings``. Pass the JSON returned by ``run_detection`` as `

Plot dimensional outlier graph for a given data point within the dataset. Parameters ---------- ind : int

The amount of variance explained by each of the selected components. Equal to n_components largest eigenvalues of the covariance matr

Percentage of variance explained by each of the selected components. If ``n_components`` is not set then all components are stored and the

The impurity-based feature importance. The higher, the more important the feature. The importance of a feature is computed as the (nor