共3个ipynb文件，包括对于数据预处理并可视化、kmeans聚类分析客户类型、用网格搜索随机森林的最佳参数并保证AUC大于0.75.
2022-01-22 19:02:18
144KB
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利用python实现随机森林算法预测信用卡违约情况，使用的是海豚大数据大数据分析赛的数据
2022-01-18 16:13:38
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Features of Random Forests
It is unexcelled in accuracy among current algorithms.
It runs efficiently on large data bases.
It can handle thousands of input variables without variable deletion.
It gives estimates of what variables are important in the classification.
It generates an internal unbiased estimate of the generalization error as the forest building progresses.
It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing.
It has methods for balancing error in class population unbalanced data sets.
Generated forests can be saved for future use on other data.
Prototypes are computed that give information about the relation between the variables and the classification.
It computes proximities between pairs of cases that can be used in clustering, locating outliers, or (by scaling) give interesting views of the data.
The capabilities of the above can be extended to unlabeled data, leading to unsupervised clustering, data views and outlier detection.
It offers an experimental method for detecting variable interactions.
2022-01-18 16:01:07
1.54MB
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AnomalyFilter
本项目应用孤立森林异常检测算法，过滤JMeter 在对Splunk 数据库进行压力测试过程中产生的异常性能数据。
背景
在压力测试和性能分析过程中，通常有两种情形会导致测试结果不准确：
一是由于JMeter 和LoadRunner 等工具在启动多线程是有一个爬坡过程，通常需要1-10秒钟左右，这个时间也不会很长。但在这个过程由于线程数量不定，导致了测试数据的可靠性降低。
二是由于现实测试中通常会存在网络异常的情形，极少量的异常值会大大影响对被测对象的性能评估。
在上述场景中，异常的数据与整个测试数据样本相比是很少的一部分，常见的分类算法例如：SVM、逻辑回归等都不合适。而孤立森林算法恰好非常适合上述场景，首先测试数据具备一定的连续性，其次异常数据具备显着的离群特征，最后异常数据的产生是小概率事件，因此，孤立森林算法在网络安全、交易欺诈、疾病监测等方面也有着广泛的
2022-01-15 14:53:42
390KB
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