异常检测算（ OneClassSVMEllipticEnvelopeIsolationForestLocalOutlierFactor）法比较，基于四个仿真数据集

Posted 2021-06-21 Data+Science+Insight

异常检测算（ OneClassSVM、EllipticEnvelope、IsolationForest、LocalOutlierFactor）法比较，基于四个仿真数据集

This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data.

# 使用数据生成模块生成四个仿真数据集（两维数据集）。数据集包含一个或者两个团（密度富集区）来考察模型应对多模态（多峰）数据的能力；

For each dataset, 15% of samples are generated as random uniform noise. This proportion is the value given to the nu parameter of the OneClassSVM and the contamination parameter of the other outlier detection algorithms. Decision boundaries between inliers and outl