Python / outlier detection

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275 (+0) ⭐

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

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AutoOut 🌿
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Automated Outlier Detection and Treatment Tool

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PyNomaly 🌿
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Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

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[NeurIPS 2019 Spotlight] High dimensional mean estimation and outlier detection in nearly-linear time.

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2820 (+186) ⭐

Anomaly detection related books, papers, videos, and toolboxes

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GAAL-based Outlier Detection

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outlier πŸ‚
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implement some outlier detection algorithms

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BNP Paribas Kaggle Data Set Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management Outlier Detection- Ensemble unsupervised learning method - Isolation Forest The isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data.

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outlier-detect 🌿
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Code that implements the novel outlier detection algorithms from my Ph.D. dissertation.

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luminol πŸ‚
794 (+0) ⭐

Anomaly Detection and Correlation library

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