如何解释R中SVM的预测结果？
我是R的新手，我正在使用如何解释R中SVM的预测结果？,r,classification,svm,R,Classification,Svm,我是R的新手，我正在使用e1071包进行R中的SVM分类 我使用了以下代码： data <- loadNumerical() model <- svm(data[,-ncol(data)], data[,ncol(data)], gamma=10) print(predict(model, data[c(1:20),-ncol(data)])) 数据中的行数为500 如上面的代码所示，我测试了前20行的预测。输出为： 1 2
e1071data <- loadNumerical()
model <- svm(data[,-ncol(data)], data[,ncol(data)], gamma=10)
print(predict(model, data[c(1:20),-ncol(data)]))
1 2 3 4 5 6 7
0.04906014 0.88230392 0.04910760 0.04910719 0.87302217 0.04898187 0.04909523
8 9 10 11 12 13 14
0.04909199 0.87224979 0.04913189 0.04893709 0.87812890 0.04909588 0.04910999
15 16 17 18 19 20
0.89837037 0.04903778 0.04914173 0.04897789 0.87572114 0.87001066
如果存在这样的变量，我如何推导它？从广义上讲，对于这样的分类器，二元响应变量的预测值可以被认为是该观测值属于类别1的概率（在本例中，类实际上标记为0/1；在其他情况下，您需要知道函数将哪个类视为1或0；R通常按字母顺序对因子的标签进行排序，因此最后一个将是类1） 因此，人们最常见的做法是使用0.5作为截止值。但我应该警告您，这个决定背后有大量的数学依据，建模环境的细节可能需要不同的截止值。使用0.5作为截止值通常是最好的做法，但支持向量机是相当复杂的；我建议您在您开始尝试将支持向量机和分类理论应用于实际数据之前，请阅读它们
我最喜欢的参考资料是Hastine、Tibshirani和Friedman的著作。由于结果变量是数值型的，因此它使用SVM的回归公式。我认为您需要分类公式。您可以通过将结果强制为一个因子或设置
type=“C-classification”> model <- svm(vs ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
0.8529506670 0.8529506670 0.9558654451 0.8423224174
Hornet Sportabout Valiant Duster 360 Merc 240D
0.0747730699 0.6952501964 0.0123405904 0.9966162477
Merc 230 Merc 280 Merc 280C Merc 450SE
0.9494836511 0.7297563543 0.6909235343 -0.0327165348
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
-0.0092851098 -0.0504982402 0.0319974842 0.0504292348
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
-0.0504750284 0.9769206963 0.9724676874 0.9494910097
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
0.9496260289 0.1349744908 0.1251344111 0.0395243313
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0.0983094417 1.0041732099 0.4348209129 0.6349628695
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0.0009258333 0.0607896408 0.0507385269 0.8664157985
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
Levels: 0 1
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars,probability=TRUE)
> predict(model,mtcars,probability=TRUE)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
attr(,"probabilities")
0 1
Mazda RX4 0.2393753 0.76062473
Mazda RX4 Wag 0.2393753 0.76062473
Datsun 710 0.1750089 0.82499108
Hornet 4 Drive 0.2370382 0.76296179
Hornet Sportabout 0.8519490 0.14805103
Valiant 0.3696019 0.63039810
Duster 360 0.9236825 0.07631748
Merc 240D 0.1564898 0.84351021
Merc 230 0.1780135 0.82198650
Merc 280 0.3402143 0.65978567
Merc 280C 0.3829336 0.61706640
Merc 450SE 0.9110862 0.08891378
Merc 450SL 0.8979497 0.10205025
Merc 450SLC 0.9223868 0.07761324
Cadillac Fleetwood 0.9187301 0.08126994
Lincoln Continental 0.9153549 0.08464509
Chrysler Imperial 0.9358186 0.06418140
Fiat 128 0.1627969 0.83720313
Honda Civic 0.1649799 0.83502008
Toyota Corolla 0.1781531 0.82184689
Toyota Corona 0.1780519 0.82194807
Dodge Challenger 0.8427087 0.15729129
AMC Javelin 0.8496198 0.15038021
Camaro Z28 0.9190294 0.08097056
Pontiac Firebird 0.8361349 0.16386511
Fiat X1-9 0.1490934 0.85090660
Porsche 914-2 0.5797194 0.42028060
Lotus Europa 0.4169587 0.58304133
Ford Pantera L 0.8731716 0.12682843
Ferrari Dino 0.8392372 0.16076281
Maserati Bora 0.8519422 0.14805785
Volvo 142E 0.2289231 0.77107694
>模型预测（模型）
马自达RX4马自达RX4 Wag Datsun 710大黄蜂4路
0.8529506670 0.8529506670 0.9558654451 0.8423224174
大黄蜂Sportabout Valiant Duster 360 Merc 240D
0.0747730699 0.6952501964 0.0123405904 0.9966162477
美世230美世280美世280C美世450SE
0.9494836511 0.7297563543 0.6909235343 -0.0327165348
美世450SL美世450SLC卡迪拉克弗利特伍德林肯大陆
-0.0092851098 -0.0504982402 0.0319974842 0.0504292348
克莱斯勒帝国菲亚特128本田思域丰田花冠
-0.0504750284 0.9769206963 0.9724676874 0.9494910097
丰田科罗纳道奇挑战者AMC标枪Camaro Z28
0.9496260289 0.1349744908 0.1251344111 0.0395243313
庞蒂亚克火鸟菲亚特X1-9保时捷914-2莲花欧罗巴
0.0983094417 1.0041732099 0.4348209129 0.6349628695
福特Pantera L法拉利迪诺玛莎拉蒂波拉沃尔沃142E
0.0009258333 0.0607896408 0.0507385269 0.8664157985
> model <- svm(vs ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
0.8529506670 0.8529506670 0.9558654451 0.8423224174
Hornet Sportabout Valiant Duster 360 Merc 240D
0.0747730699 0.6952501964 0.0123405904 0.9966162477
Merc 230 Merc 280 Merc 280C Merc 450SE
0.9494836511 0.7297563543 0.6909235343 -0.0327165348
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
-0.0092851098 -0.0504982402 0.0319974842 0.0504292348
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
-0.0504750284 0.9769206963 0.9724676874 0.9494910097
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
0.9496260289 0.1349744908 0.1251344111 0.0395243313
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0.0983094417 1.0041732099 0.4348209129 0.6349628695
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0.0009258333 0.0607896408 0.0507385269 0.8664157985
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
Levels: 0 1
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars,probability=TRUE)
> predict(model,mtcars,probability=TRUE)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
attr(,"probabilities")
0 1
Mazda RX4 0.2393753 0.76062473
Mazda RX4 Wag 0.2393753 0.76062473
Datsun 710 0.1750089 0.82499108
Hornet 4 Drive 0.2370382 0.76296179
Hornet Sportabout 0.8519490 0.14805103
Valiant 0.3696019 0.63039810
Duster 360 0.9236825 0.07631748
Merc 240D 0.1564898 0.84351021
Merc 230 0.1780135 0.82198650
Merc 280 0.3402143 0.65978567
Merc 280C 0.3829336 0.61706640
Merc 450SE 0.9110862 0.08891378
Merc 450SL 0.8979497 0.10205025
Merc 450SLC 0.9223868 0.07761324
Cadillac Fleetwood 0.9187301 0.08126994
Lincoln Continental 0.9153549 0.08464509
Chrysler Imperial 0.9358186 0.06418140
Fiat 128 0.1627969 0.83720313
Honda Civic 0.1649799 0.83502008
Toyota Corolla 0.1781531 0.82184689
Toyota Corona 0.1780519 0.82194807
Dodge Challenger 0.8427087 0.15729129
AMC Javelin 0.8496198 0.15038021
Camaro Z28 0.9190294 0.08097056
Pontiac Firebird 0.8361349 0.16386511
Fiat X1-9 0.1490934 0.85090660
Porsche 914-2 0.5797194 0.42028060
Lotus Europa 0.4169587 0.58304133
Ford Pantera L 0.8731716 0.12682843
Ferrari Dino 0.8392372 0.16076281
Maserati Bora 0.8519422 0.14805785
Volvo 142E 0.2289231 0.77107694
>模型预测（模型）
马自达RX4马自达RX4 Wag Datsun 710大黄蜂4路
1 1 1 1
大黄蜂Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
美世230美世280美世280C美世450SE
1 1 1 0
美世450SL美世450SLC卡迪拉克弗利特伍德林肯大陆
0 0 0 0
克莱斯勒帝国菲亚特128本田思域丰田花冠
0 1 1 1
丰田科罗纳道奇挑战者AMC标枪Camaro Z28
1 0 0 0
庞蒂亚克火鸟菲亚特X1-9保时捷914-2莲花欧罗巴
0 1 0 1
福特Pantera L法拉利迪诺玛莎拉蒂波拉沃尔沃142E
0 0 0 1
级别：0 1
> model <- svm(vs ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
0.8529506670 0.8529506670 0.9558654451 0.8423224174
Hornet Sportabout Valiant Duster 360 Merc 240D
0.0747730699 0.6952501964 0.0123405904 0.9966162477
Merc 230 Merc 280 Merc 280C Merc 450SE
0.9494836511 0.7297563543 0.6909235343 -0.0327165348
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
-0.0092851098 -0.0504982402 0.0319974842 0.0504292348
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
-0.0504750284 0.9769206963 0.9724676874 0.9494910097
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
0.9496260289 0.1349744908 0.1251344111 0.0395243313
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0.0983094417 1.0041732099 0.4348209129 0.6349628695
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0.0009258333 0.0607896408 0.0507385269 0.8664157985
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars)
> predict(model)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
Levels: 0 1
> model <- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars,probability=TRUE)
> predict(model,mtcars,probability=TRUE)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
1 1 1 1
Hornet Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
Merc 230 Merc 280 Merc 280C Merc 450SE
1 1 1 0
Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
0 0 0 0
Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
0 1 1 1
Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
1 0 0 0
Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
0 1 0 1
Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
0 0 0 1
attr(,"probabilities")
0 1
Mazda RX4 0.2393753 0.76062473
Mazda RX4 Wag 0.2393753 0.76062473
Datsun 710 0.1750089 0.82499108
Hornet 4 Drive 0.2370382 0.76296179
Hornet Sportabout 0.8519490 0.14805103
Valiant 0.3696019 0.63039810
Duster 360 0.9236825 0.07631748
Merc 240D 0.1564898 0.84351021
Merc 230 0.1780135 0.82198650
Merc 280 0.3402143 0.65978567
Merc 280C 0.3829336 0.61706640
Merc 450SE 0.9110862 0.08891378
Merc 450SL 0.8979497 0.10205025
Merc 450SLC 0.9223868 0.07761324
Cadillac Fleetwood 0.9187301 0.08126994
Lincoln Continental 0.9153549 0.08464509
Chrysler Imperial 0.9358186 0.06418140
Fiat 128 0.1627969 0.83720313
Honda Civic 0.1649799 0.83502008
Toyota Corolla 0.1781531 0.82184689
Toyota Corona 0.1780519 0.82194807
Dodge Challenger 0.8427087 0.15729129
AMC Javelin 0.8496198 0.15038021
Camaro Z28 0.9190294 0.08097056
Pontiac Firebird 0.8361349 0.16386511
Fiat X1-9 0.1490934 0.85090660
Porsche 914-2 0.5797194 0.42028060
Lotus Europa 0.4169587 0.58304133
Ford Pantera L 0.8731716 0.12682843
Ferrari Dino 0.8392372 0.16076281
Maserati Bora 0.8519422 0.14805785
Volvo 142E 0.2289231 0.77107694
>模型预测（模型，mtcars，概率=真）
马自达RX4马自达RX4 Wag Datsun 710大黄蜂4路
1 1 1 1
大黄蜂Sportabout Valiant Duster 360 Merc 240D
0 1 0 1
美世230美世280美世280C美世450SE
1 1 1 0
美世450SL美世450SLC卡迪拉克弗利特伍德林肯大陆
0 0 0 0
克莱斯勒帝国菲亚特128本田思域丰田花冠
0 1 1 1
丰田科罗纳道奇挑战者AMC标枪Camaro Z28
1 0 0 0
庞蒂亚克火鸟菲亚特X1-9保时捷914-2莲花欧罗巴
0