odd ratio

基本解释

To univariate analyze for 10 kinds probable risk factors, there were the significant differences on 5 kinds risk factors (P0.05), odd ratio (OR>1).
对10种可能的危险因素进行单因素分析，有5种呈现显著性差异（P0 .0 5 ），比值比（OR）均大于1。
At the same time, compare to traditional feature selection methods : odd ratio and information gain, the normal-based method yields better classification performance.
基于法向量的特征选择算法可以在较大幅度减少计算资源消耗的同时基本维持所得到的分类器性能。
Fourthly, for linear SVM classifier, explore the performance of normal-based feature selection method by comparing it with two traditional feature selection methods: odd ratio and information gain.
第四，研究对于线性SVM分类器，选用基于法向量的特征选择算法，和传统的基于几率比和基于信息增益的特征选择算法，对文本分类性能的影响。