`data(iris) attach(iris) model <- svm(Species ~ ., data = iris) x <- subset(iris, select = -Species) y <- Species model <- svm(x, y, probability = TRUE)`

This model is correctly fitted. However

Does not show the correct values

pred <- predict(model, x)

We see that every prediction is setosa, even though the dataset is equally divided between the three classes (setosa, versicolor and virginica). It seems that something went wrong with the direct prediction, but one way to overcome this problem is to use the predicted probabilities, that seem to be well computed:

`> pred 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa Levels: setosa versicolor virginica`

Observe how they look

pred <- predict(model, x, decision.values = TRUE, probability = TRUE)

Now we see that the most probable class is indeed the ground truth and we can correctly classify with the following function

> attr(pred, "probabilities") setosa versicolor virginica 1 0.980325653 0.011291686 0.008382661 2 0.972927977 0.018061300 0.009010723 3 0.979044327 0.011921880 0.009033793 ... 48 0.977140826 0.013050710 0.009808464 49 0.977831001 0.013359834 0.008809164 50 0.980099521 0.011501036 0.008399444 51 0.017740468 0.954734399 0.027525133 52 0.010394167 0.973918376 0.015687457 ... 97 0.009263806 0.986123276 0.004612918 98 0.008503724 0.988168405 0.003327871 99 0.025068812 0.965415124 0.009516064 100 0.007514580 0.987584706 0.004900714 101 0.012482541 0.002502134 0.985015325 ... 149 0.013669944 0.017618659 0.968711397 150 0.010205071 0.140882630 0.848912299

predsvm<-function(model,newdata)One might also program the following function, that deals with the way the support vector coefficients are stored in the

{

prob<-attr(predict(model, newdata, probability = TRUE),"probabilities")

n<-dim(prob)[1]

m<-dim(prob)[2]

me<-which(prob==apply(prob,1,max))

return(as.factor(model$labels[floor((me-1)/n)+1]))

}

*model*object, in

*model$coefs*and

*model$rho*:

```
``````
## Linear Kernel function
K <- function(i,j) crossprod(i,j)
predsvm <- function(object, newdata) {
## compute start-index
start <- c(1, cumsum(object$nSV)+1)
start <- start[-length(start)]
## compute kernel values
kernel <- sapply (1:object$tot.nSV,
function (x) K(object$SV[x,], newdata))
## compute raw prediction for classifier (i,j)
predone <- function (i,j) {
## ranges for class i and j:
ri <- start[i] : (start[i] + object$nSV[i] - 1)
rj <- start[j] : (start[j] + object$nSV[j] - 1)
## coefs for (i,j):
coef1 <- object$coefs[ri, j-1]
coef2 <- object$coefs[rj, i]
## return raw values:
crossprod(coef1, kernel[ri]) + crossprod(coef2, kernel[rj])
}
## compute votes for all classifiers
votes <- rep(0,object$nclasses)
c <- 0 # rho counter
for (i in 1 : (object$nclasses - 1))
for (j in (i + 1) : object$nclasses)
if (predone(i,j) > object$rho[c <- c + 1])
votes[i] <- votes[i] + 1
else
votes[j] <- votes[j] + 1
## return winner (index with max. votes)
object$levels[which(votes %in% max(votes))[1]]
}
```

Thanks for the blog. It really helped.

ReplyDeleteyou might want to emphasize on the fact that we need to set 'probability = TRUE' for both training the model & in 'predict' api. First time I saw ur blog I missed the point that we should set it while training the model too. Took me bit more focus to get it right :)

ReplyDeleteNice tutorial. I've 40,000 training and 40,000 testing dataset having 1024 attributes having 397 classes, I want to use this concept. Does it work?

ReplyDelete