如何解释R中SVM的预测结果？ [英] How to Interpret Predict Result of SVM in R?
问题描述
我是R的新手，并且正在使用 e1071 软件包在R中进行SVM分类。
I'm new to R and I'm using the e1071 package for SVM classification in R.
我使用以下代码：
data <- loadNumerical()
model <- svm(data[,-ncol(data)], data[,ncol(data)], gamma=10)
print(predict(model, data[c(1:20),-ncol(data)]))
loadNumerical 用于加载数据，数据的格式为（输入前8列，最后一列为分类）：
The loadNumerical is for loading data, and the data are of the form(first 8 columns are input and the last column is classification) :
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
1 39 1 -1 43 -1 1 0 0.9050497 0
2 23 -1 -1 30 -1 -1 0 1.6624974 1
3 50 -1 -1 49 1 1 2 1.5571429 0
4 46 -1 1 19 -1 -1 0 1.3523685 0
5 36 1 1 29 -1 1 1 1.3812029 1
6 27 -1 -1 19 1 1 0 1.9403649 0
7 36 -1 -1 25 -1 1 0 2.3360004 0
8 41 1 1 23 1 -1 1 2.4899738 0
9 21 -1 -1 18 1 -1 2 1.2989637 1
10 39 -1 1 21 -1 -1 1 1.6121595 0
数据中的行数为500。
The number of rows in the data is 500.
如图所示在上面的代码中，我测试了前20行以进行预测。输出为：
As shown in the code above, I tested the first 20 rows for prediction. And the output is:
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
我可以从结果中直观地看出，当结果接近0时，它表示0类，如果接近1，则在1类中。
I can tell intuitively from the result that when the result is close to 0, it means 0 class, and if it's close to 1 it's in the 1 class.
但是我的问题是如何精确解释结果：是否可以使用阈值 s 因此 s 以下的值归为0，而 s 以上的值归为1？
But my question is how can I precisely interpret the result: is there a threshold s I can use so that values below s are classified as 0 and values above s are classified as 1 ?
这样的 s ，我怎么得出呢？
If there exists such s, how can I derive it ?
推荐答案
由于结果变量为数字，因此使用SVM的回归公式。我认为您需要分类表述。您可以通过将结果强制为一个因素或设置 type = C-classification 来更改此设置。
Since your outcome variable is numeric, it uses the regression formulation of SVM. I think you want the classification formulation. You can change this by either coercing your outcome into a factor, or setting type="C-classification".
回归：
> 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
分类：
> 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
此外，如果您希望概率作为预测而不是原始分类，您可以通过配合概率选项来做到这一点。
Also, if you want probabilities as your prediction rather than just the raw classification, you can do that by fitting with the probability option.
具有概率：
> 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
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