DATA MINING
Desktop Survival Guide by Graham Williams |
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Step by Step |
The learner deployed in the AdaBoost algorithm is typically a decision tree learner that builds no more than a single split decision tree (also called a decision stump). Such a decision tree can be built in R using rpart and we illustrate this in the following code segments.
First we load the wine dataset and extract the input
variables () and the output variable (). For a simple
application of the algorithm, we'll have only a binary output
(predicting ), and again for mathematical convenience we'll
predict 1 or -1:
> library(rpart) > load("wine.RData") > N <- nrow(wine) # 178 > M <- ncol(wine) # 14 > x <- as.matrix(wine[,2:M]) > y <- as.integer(wine[,1]) > y[y>1] <- -1 > y [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [...] [176] -1 -1 -1 |
> w <- rep(1/N, N) > w [1] 0.005617978 0.005617978 0.005617978 0.005617978 0.005617978 0.005617978 [...] [175] 0.005617978 0.005617978 0.005617978 0.005617978 > control <- rpart.control(maxdepth=1) > M1 <- rpart(y ~ x, weights=w/mean(w), control=control, method="class") > M1 n= 178 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 178 59 -1 (0.66853933 0.33146067) 2) x.Proline< 755 111 2 -1 (0.98198198 0.01801802) * 3) x.Proline>=755 67 10 1 (0.14925373 0.85074627) * |
We now need to find those entities which are incorrectly classified by
the model. The R code here calls predict to apply the
model M1 to the dataset it was built from. From this result we get the
second column which is the list of probabilities for each observation being
in class 1. If this probability is above 0.5 then the result is 1,
otherwise it is -1 (multiplying the logical value by 2 and then
subtracting 1 achieves this since TRUE is regarded as 1 and FALSE as
0). The resulting class is then compared to the y's and
which returns the index of those entities for which the
prediction differs from the actual class.
> ms <- which(((predict(M1)[,2]>0.5)*2)-1 != y) > names(ms) <- NULL > ms [1] 5 44 71 74 75 96 142 145 146 158 176 177 |
We can now calculate the model weight and update the observation weights,
dividing by the resulting sum of weights to get a normalised value (so
that sum(w) is 1:
> e1 <- sum(w[ms])/sum(w) # 0.06741573 > a1 <- log((1-e1)/e1) # 2.627081 > w[ms] <- w[ms]*exp(a1) > w[ms] [1] 0.07771536 0.07771536 0.07771536 0.07771536 0.07771536 0.07771536 [7] 0.07771536 0.07771536 0.07771536 0.07771536 0.07771536 0.07771536 |
We build our second model:
> M2 <- rpart(y ~ x, weights=w/mean(w), control=control, method="class") > M2 n= 178 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 178 45.3935700 -1 (0.744979920 0.255020080) 2) x.Flavanoids< 2.31 101 0.5361446 -1 (0.995381062 0.004618938) * 3) x.Flavanoids>=2.31 77 17.0672700 1 (0.275613276 0.724386724) * > ms <- which(((predict(M2)[,2]>0.5)*2)-1 != y) > names(ms) <- NULL > ms [1] 28 64 66 67 72 74 80 82 98 99 100 110 111 121 122 124 125 126 127 [20] 129 > e2 <- sum(w[ms])/sum(w) # 0.09889558 > a2 <- log((1-e2)/e2) # 2.209557 > w[ms] <- w[ms]*exp(a2) > w[ms] [1] 0.05118919 0.05118919 0.05118919 0.05118919 0.05118919 0.70811707 [7] 0.05118919 0.05118919 0.05118919 0.05118919 0.05118919 0.05118919 [13] 0.05118919 0.05118919 0.05118919 0.05118919 0.05118919 0.05118919 [19] 0.05118919 0.05118919 |
And then our third model:
> M3 <- rpart(y ~ x, weights=w/mean(w), control=control, method="class") > M3 n= 178 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 178 27.60091 -1 (0.84493870 0.15506130) 2) x.Proline< 987.5 134 12.09805 -1 (0.92554915 0.07445085) * 3) x.Proline>=987.5 44 0.00000 1 (0.00000000 1.00000000) * > ms <- which(((predict(M3)[,2]>0.5)*2)-1 != y) > names(ms) <- NULL > ms [1] 5 20 21 22 25 26 29 36 37 40 41 44 45 48 57 > e3 <- sum(w[ms])/sum(w) > e3 [1] 0.06796657 > a3 <- log((1-e3)/e3) > a3 [1] 2.618353 |
The final model, if we chose to stop here, is then:
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