代码搜索结果

Construct 21 KNN models, each with up to 6 inputs selected from 6 candidates... Selecting input 1: Model 1/21:1 --> Recognition rate = 51.0% Model 2/21:2 --> Recognition rate = 51.5% Model 3/2

Construct 63 KNN models, each with up to 6 inputs selected from 13 candidates... Selecting input 1: Model 1/63: 1 --> Recognition rate = 59.6% Model 2/63: 2 --> Recognition rate = 55.1% Model

Construct 15 KNN models, each with up to 4 inputs selected from 4 candidates... modelIndex 1/15: 1 --> Recognition rate = 58.666667% modelIndex 2/15: 2 --> Recognition rate = 48.000000% modelInde

Construct 10 KNN models, each with up to 4 inputs selected from 4 candidates... Selecting input 1: Model 1/10:1 --> Recognition rate = 58.7% Model 2/10:2 --> Recognition rate = 48.0% Model 3/1

Construct 21 KNN models, each with up to 6 inputs selected from 6 candidates... Selecting input 1: Model 1/21:1 --> Recognition rate = 48.0% Model 2/21:2 --> Recognition rate = 55.0% Model 3/2

Construct 10 KNN models, each with up to 4 inputs selected from 4 candidates... Selecting input 1: Model 1/10:sepal length --> Recognition rate = 58.7% Model 2/10:sepal width --> Recognition ra

Construct 63 KNN models, each with up to 6 inputs selected from 13 candidates... Selecting input 1: Model 1/63: 1 --> Recognition rate = 59.6% Model 2/63: 2 --> Recognition rate = 56.2% Model

Use of KNNR for Iris data: Size of design set (odd-indexed data)= 75 Size of test set (even-indexed data) = 75 Recognition rates as K varies: 1-NNR ===> 1-3/75 = 96.00%. 2-NNR ===> 1-2/75 =

Use of KNNR for Iris data: Size of design set (odd-indexed data)= 75 Size of test set (even-indexed data) = 75 After k-means clustering, size of design set = 9 Recognition rate = 97.3333%

Recognition rate = 98.000000%