Measuring Software Reliability: A Trend Using Machine Learning Techniques - Complex System Modelling and Control Through Intelligent Soft Computations - page 811

# Check accuracy:
table(pred, y)
# compute decision values and probabilities:
pred <

predict(model, x, decision.values = TRUE)

-

attr(pred, )[1:4,]

# visualize (classes by color, SV by crosses):
plot(cmdscale(dist(iris,[

“ decision.values ”

5])),

−

col = as.integer(iris,[5]),
pch = c( )[1:150 tune 53

## try regression mode on two dimensions
# create data
x<

“ o ” , “ + ”

seq (0.1, 5, by = 0.05)

-

y< log(x) + rnorm(x, sd = 0.2)

# estimate model and predict input values
m<

-

- svm(x, y)

new < predict(m, x)

# visualize
plot(x, y)
points(x, log(x), col = 2)
points(x, new, col = 4)
## density-estimation
# create 2-dim. normal with rho = 0:
X<

-

data.frame(a = rnorm(1000), b = rnorm(1000))
attach(X)
# traditional way:
m<

-

svm(X, gamma = 0.1)
# formula interface:
m<

-

svm( ., data = X, gamma = 0.1)

- *

# or:
m< svm( a + b, gamma = 0.1)

- *

# test:
newdata < data.frame(a = c(0, 4), b = c(0, 4))

predict (m, newdata)
# visualize:
plot(X, col = 1:1000 points(newdata, pch =

-

“ + ” , col = 2, cex = 5)

# weights: (example not particularly sensible)
i2 < iris

levels(i2$Species)[3] <

-

- “ versicolor ”

summary(i2$Species)
wts < - 100 /table(i2$Species)

wts
m< - svm(Species * ., data = i2, class.weights = wts)