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command_notes.txt
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command_notes.txt
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library(caret)
options(max.print=100)
training = read.csv("pml-training.csv")
!!!!!There are a bunch of worthless columns
nsv = nearZeroVar(pmltraining,saveMetrics=TRUE)
colSums(is.na(training))
training1<-training[,colSums(!is.na(training))>19000]
training2<-training1[,colSums(training1!="")>19000]
training3 <- training2[,8:60]
!!!TODO: NEED TO REMOVE STRONGLY CORRELATED VARIABLES
M <- abs(cor(training3[,-53]))
diag(M) <- 0
which(M > 0.8,arr.ind=T)
drops <- c("accel_belt_x","accel_belt_y","accel_belt_z","gyros_dumbbell_x","gyros_dumbbell_z","gyros_arm_y","yaw_belt","total_accel_belt","magnet_belt_x","magnet_arm_x","magnet_arm_z","accel_dumbbell_x","accel_dumbbell_z","gyros_forearm_y")
training4 <- training3[,!(names(training3) %in% drops)]
//smalltraining = head(training, 200)
inTrain<-createDataPartition(y=training4$classe,p=0.1,list=FALSE)
smalltraining<-training4[inTrain,]
pmltraining2 <- apply(pmltraining, 2, as.numeric)
pmltraining2[is.na(pmltraining2)] <- 0
prComp <- prcomp(pmltraining2)
train()
Method: RF, CV
model<-train(classe~.,data=smalltraining,method="rf",trControl=trainControl(method="cv",number=3),allowParallel=TRUE)
preProcValues <- preProcess(training4, method = c("center", "scale"))
traininingTransformed <- predict(preProcValues, training3)
testingTransformed <- predict(preProcValues, testing)
tc <- trainControl("repeatedcv", number=10, repeats=10)
//model <- train(classe ~., data=training3, method="rf", trControl=tc, preProc=c("center", "scale"))
model <- train(classe ~., data=trainingTransformed, method="rf", trControl=tc)
as.data.frame.matrix(mytable)
Or just as.data.frame(mytable). (is.matrix(mytable) will reveal that tables really are just dressed up matrices, and as.data.frame.matrix is the method that gets dispatched when as.data.frame() is passed a matrix argument.)
> system.time(model_rf <- train(classe ~ ., data = training, method = "rf"))
user system elapsed
37.307 5.551 1051.359
> system.time(model_rf <- randomForest(classe ~ ., data = training))
user system elapsed
25.232 0.153 25.641