问题描述
关于 Weka 中的权重，我需要您的帮助.我正在对大量数据进行一些实验:我正在将数据转换为实例并使用不同的分类器进行研究.现在我想检查赋予实例权重如何影响学习 - 有时我想赋予实例权重，有时则不.我的问题是:
I need your help regarding weights in Weka.I am running some experiments on large scale of data: I am translating the data into instances and use different classifiers in order to study.Now I want to examine how entitling weights to instances effects the studying- sometimes I want to entitle an instance with a weight and sometimes not.My question is:
- 可能的权重范围是多少?
- 权重的影响是否因分类器而异?
- 是否有默认权重(我在某处看到它可能是 1，但我想确认一下)?
- 对相关信息的任何引用将不胜感激:)
推荐答案
问题 2 的答案是是"，这也会影响问题 1 的答案.基本上，Weka 只将权重传递给实际的分类算法.允许的权重范围及其使用方式完全取决于分类器的实现.关于问题 3，默认权重会赋予所有实例相同的权重，实际数量并不那么重要.
The answer to question 2 is "yes", and that also affects the answer to question 1. Basically, Weka only passes the weights on to the actual classification algorithm. The range of allowed weights and how they are used depends entirely on the implementation of the classifier. Regarding question 3, the default weight will give equal weight to all instances, the actual number is not that important.
例如，最近邻分类器完全忽略权重，即使它很乐意接受任何权重值.理论上，可以实现最近邻分类器来考虑权重，但这个特定的分类器没有.因此，问题 2 的答案是它实际上比分类器算法更取决于分类器的特定实现.
For example the nearest neighbour classifier ignores weights completely, even though it will happily take any weight values. In theory, nearest neighbour classifiers could be implemented to consider weights, but this particular one doesn't. So the answer to question 2 would be that it actually depends on the particular implementation of the classifier even more than the classifier algorithm.
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