polynomial reg 150...60 70 80 90 0 50 100 150 x y Polynomial Regression (degree=2) 60 70 80 90 0 50...
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60 70 80 90 0 50 100 150 x y polynomial reg LOESS Smoothing spline
Transcript of polynomial reg 150...60 70 80 90 0 50 100 150 x y Polynomial Regression (degree=2) 60 70 80 90 0 50...
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# Prepare the spam data: classify Y based on 57 X’s > spam=read.table("spam.txt"); > spam=data.frame(spam); > spam.name=read.table("spam_name.txt", as.is=TRUE); > for(i in 1:57) names(spam)[i]=as.character(spam.name[i,]); > names(spam)[58]="Y"; > spam$Y=as.factor(spam$Y); # Split data into training and testing > load("spam_ID.Rdata"); > spam.training = spam[ train.id, ]; > spam.test = spam[ test.id, ]; > p = dim(spam)[2] - 1;
> library(randomForest); > rfModel = randomForest(Y~., data = spam.training, importance = T, mtry = 7, ntree=400 ); #mtry is, by default, sqrt(p) for classification > yhat.train = predict(rfModel, spam.training ) > table(spam.training$Y, yhat.train); yhat.train 0 1 0 364 0 1 1 235 > 1/(365+235) [1] 0.001666667 > table(rfModel$predicted, spam.training$Y) 0 1 0 349 30 1 15 206
> rfModel$confusion 0 1 class.error 0 349 15 0.04120879 1 30 206 0.12711864 > (45)/(45+349+206) [1] 0.075 > yhat.test = predict(rfModel, spam.test) > table(spam.test$Y, yhat.test); yhat.test 0 1 0 236 6 1 19 139 > (25)/(236+6+19+139) [1] 0.0625
# Check the possible outcomes returned by randomForest > names(rfModel) > ?randomForest > rfModel$oob.times [1] 148 145 144 143 ….. > tmp=rfModel$err.rate > tmp[c(1:5,397:400),] OOB 0 1 [1,] 0.17592593 0.15447154 0.2043011 [2,] 0.19726027 0.17061611 0.2337662 [3,] 0.19325843 0.17110266 0.2252747 [4,] 0.16566866 0.15384615 0.1831683 [5,] 0.16141002 0.14814815 0.1813953 [6,] 0.07833333 0.04120879 0.1355932 [7,] 0.07666667 0.04120879 0.1313559 [8,] 0.07666667 0.04120879 0.1313559 [9,] 0.07833333 0.04120879 0.1355932
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> round(rfModel$importance, dig=3) 0 1 MeanDecreaseAccuracy MeanDecreaseGini Wmake 0.001 0.001 0.001 2.481 Waddress 0.001 0.000 0.001 1.541 Wall 0.001 0.003 0.002 3.137 Cdollar 0.017 0.029 0.022 21.712 Cpound 0.001 0.000 0.000 1.293 CAPave 0.023 0.018 0.021 14.539 CAPlongest 0.032 0.026 0.029 15.064 CAPtotal 0.022 0.032 0.026 15.368 > sortedImpt_scaled = sort(importance(rfModel, scale = T)[,3], decreasing = T ) > sortedImp_gini = sort(rfModel$importance[,4], decreasing=T); > par( mfrow = c(2,1) ); > plot(sortedImpt_scaled, ylim=c(-.1, max(sortedImpt_scaled)+.1), type="h"); > text(1:p, sortedImpt_scaled, substr(names(sortedImpt_scaled), 2, 5), col=3); > title("importance measured by accuracy - scaled"); > grid(20); > plot(sortedImp_gini, ylim=c(-1, max(sortedImp_gini)+1), type="h"); > text(1:p, sortedImp_gini, substr(names(sortedImp_gini), 2, 5), col=3); > title("importance measured by gini"); > grid(20);
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freedollAPloAPtohpAPavourgeor000monehpleduyouyour1999650intebusipareemaioverrecredmeetsemi85techrecemailpmlabswilllabbractelndiremakealladdrorigdatapeopfont415ordecsaddrpoun8573dconftablpartrepoproj
importance measured by accuracy - scaled
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your000hpmoneyougeorpareemaioverhplintemailbusieduwill1999allmakererecesemi650addrlabstechpmpounmeetcredordepeoporigbrac85telnprojlabfont3ddatapartdirerepoaddr415conftablcs857
importance measured by gini
