随机森林微生物
在微生物群落中运行随机森林的演示和代码
2022-01-23 21:46:15
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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
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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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