Adaptive Confidence Intervals for the Test Error in Classification
نویسنده
چکیده
Sample Size n = 30 n = 100 n = 250 Data Set / Method ACI Yang Jiang ACI Yang Jiang ACI Yang Jiang ThreePt .976 .893* .914* .961 .552* .945 .961 .387* .930* Magic .955 .999* .983* .977* .991* .969* .972* .997* .974* Mam. .957 .989* .966 .962 .996* .964 .960 .995* .968 Ion. .947 .995* .985* .948 .996* .970* .970 .990* .970* Donut .968 .966 .908* .969 .851* .971* .973* .898* .966 Bal. .979* .996* .972* .988* .999* .982* .976* .996* .976* Liver .946 .995* .972* .973* .995* .974* .977* .993* .984* Spam .985* .999* .981* .983* .996* .967 .983* .990* .973* Quad .978* .997* .945 .957 .989* .938 .965 .999* .940 Heart .960 .995* .976* .949 .991* .979* .971* .989* .974*
منابع مشابه
Discussion of Adaptive confidence intervals for the test error in classification by Laber and Murphy
Let me begin by congratulating the authors on the substantial progress that they have made on an important and challenging problem in classification. They provide penetrating insight into the reasons why the most obvious methods for constructing confidence intervals for the test error have poor coverage properties, and propose an innovative and effective solution that can readily be adopted in ...
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