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本文介绍了孤立森林算法（Isolation Forest）在异常检测中的应用。 该算法通过构建多棵孤立树，基于异常点更容易被快速隔离的特性来识别异常值，具有线性时间复杂度和高精准度等优势。 前言 孤立森林，一种非常高效快速的异常检测算法 开始探索 scikit-learn import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import IsolationForest rng = np.ran 孤立森林通过随机选择一个特征，然后随机选择该特征的最大值和最小值之间的分割值来“隔离”观测值。 由于递归分区可以表示为树结构，因此隔离一个样本所需的分割次数等同于从根节点到终止节点的路径长度。 孤立森林是一种高效的异常检测算法，通过构建随机决策树快速识别异常值。本文提供Java实现示例，分析复杂度，并展示金融风控、工业质检等应用场景。包含并行训练、内存优化等性能调优技巧，适合处理高维大数据。
孤立森林，不是描述正常的样本点，而是要孤立异常点，由周志华教授等人于2008年在第八届IEEE数据挖掘国际会议上提出。 iForest（IsolationForest）孤立森林是一个基于Ensemble 的快速异常检测方法，具有线性时间复杂度和高精准度，是符合大数据处理要求的state-of-the-art算法。 本文介绍的一个比较经典用于找到数据中的孤立点的算法，你可以直接用来识别训练数据中的孤立点和噪声点，也可以使用这个算法作为前置算法帮助过滤数据。 与传统基于距离或密度的方法不同，孤立森林使用树结构对数据进行分割，并利用路径长度作为判断依据。 本文将带你：手动实现完整的孤立森林算法，加强理解孤立森林的基本原理。
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