︠4afaa026-aea6-482d-ae53-fc2f5e55720ds︠
%r
titanic <- read.table('titanic.csv', header=TRUE, sep=",", row.names=1)
library(randomForest)
set.seed(2017)
fit <- randomForest(as.factor(survived) ~ ., data=titanic, importance=TRUE, ntree=2000)
print(fit)
importance(fit)
︡52e1e83b-373a-45ce-a51f-a5245a3131ca︡{"stdout":"\nCall:\n randomForest(formula = as.factor(survived) ~ ., data = titanic,      importance = TRUE, ntree = 2000) \n               Type of random forest: classification\n                     Number of trees: 2000\nNo. of variables tried at each split: 1\n\n        OOB estimate of  error rate: 21.66%\nConfusion matrix:\n     no yes class.error\nno  791  26  0.03182375\nyes 259 240  0.51903808\n"}︡{"html":"<table>\n<thead><tr><th></th><th scope=col>no</th><th scope=col>yes</th><th scope=col>MeanDecreaseAccuracy</th><th scope=col>MeanDecreaseGini</th></tr></thead>\n<tbody>\n\t<tr><th scope=row>class</th><td>48.50761  </td><td>46.09300  </td><td>49.88664  </td><td> 42.708540</td></tr>\n\t<tr><th scope=row>age</th><td>28.41795  </td><td>25.60417  </td><td>33.45813  </td><td>  6.814097</td></tr>\n\t<tr><th scope=row>gender</th><td>61.48417  </td><td>69.81685  </td><td>67.37503  </td><td>108.850507</td></tr>\n</tbody>\n</table>\n"}︡{"done":true}
︠813e0551-d672-438f-9b19-64c3731b3245s︠
%r
library(rpart)
fit <- rpart(survived ~ ., data=titanic)
plot(fit, main="Titanic Survival")
text(fit, use.n=TRUE)
︡e2f62b26-b577-4757-8e2e-80561c3d2324︡{"file":{"filename":"/tmp/tmpj58E9H.png","show":false,"uuid":"0888a6b7-047d-41d6-9bb6-953665925d58"},"once":false}︡{"html":"<img src=\"/blobs//tmp/tmpj58E9H.png?uuid=0888a6b7-047d-41d6-9bb6-953665925d58\" smc-image-scaling=\"0.5\" />"}︡{"done":true}
︠2ab56250-d9a4-4e19-bed1-ebf75aab1fc7︠











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	no	yes	MeanDecreaseAccuracy	MeanDecreaseGini
class	48.50761	46.09300	49.88664	42.708540
age	28.41795	25.60417	33.45813	6.814097
gender	61.48417	69.81685	67.37503	108.850507