Predictive power of individual predictors: Predictors in the SVM classifier ranked based on their predictive power (from highest to lowest), using the relative weights in the SVM classifier (left), as well the % reduction in the performance based on permutation feature importance method (Right).
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接：
https://figshare.com/articles/dataset/Predictive_power_of_individual_predictors_Predictors_in_the_SVM_classifier_ranked_based_on_their_predictive_power_from_highest_to_lowest_using_the_relative_weights_in_the_SVM_classifier_left_as_well_the_reduction_in_the_performance_based_on/26523085
下载链接
链接失效反馈官方服务：
资源简介：
Predictive power of individual predictors: Predictors in the SVM classifier ranked based on their predictive power (from highest to lowest), using the relative weights in the SVM classifier (left), as well the % reduction in the performance based on permutation feature importance method (Right).
单个预测器的预测能力：支持向量机（Support Vector Machine, SVM）分类器中的各预测器按预测能力从高到低排序，左侧图表以SVM分类器中的相对权重作为排序依据，右侧图表则基于置换特征重要性（permutation feature importance）方法计算得到的性能降幅百分比进行排序。
创建时间：
2024-08-08
