Ding-Ye/Outlier-Detection-Algorithm---Isolation-Forests

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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