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204 lines
8.2 KiB
204 lines
8.2 KiB
source("04_validation_croisee_code.R")
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source("05_d_svd_mca_code.R")
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source("15_loocv_code.R")
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source("19_nystroem_approximation_code.R")
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dataset.housing <-
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function()
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{
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set.seed(1123)
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# loading dataset in memory
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dat <- read.csv(file="data/housing.csv", header=TRUE)
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# transform ocean_proximity into a factor
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dat$ocean_proximity <- as.factor(dat$ocean_proximity)
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="<1H OCEAN"] <- "O:<1H"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="ISLAND"] <- "O:ISL"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="INLAND"] <- "O:INL"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR BAY"] <- "O:NB"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR OCEAN"] <- "O:NO"
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# replace total_bedrooms missing values with the median value
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dat$total_bedrooms[is.na(dat$total_bedrooms)] <- median(dat$total_bedrooms, na.rm=TRUE)
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# remove individuals corresponding to the Island modality of ocean_proximity
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dat <- dat[dat$ocean_proximity != "O:ISL", ]
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# remove empty modalities (here, only "O:ISL")
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dat <- droplevels(dat)
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# introduce new variable for number of rooms by households
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dat['rooms'] <- dat['total_rooms'] / dat['households']
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# introduce new variable for number of bedrooms by households
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dat['bedrooms'] <- dat['total_bedrooms'] / dat['households']
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# introduce new variable for number of people by households
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dat['pop'] <- dat['population'] / dat['households']
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# remove individuals with extremely high values of the target
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dat <- dat[dat$median_house_value < 500001, ]
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# select variables to retain in the dataset
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dat <- dat[c('longitude', 'latitude', 'housing_median_age', 'households',
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'median_income', 'median_house_value', 'ocean_proximity',
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'rooms', 'bedrooms', 'pop')]
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# one-hot-enconde categorical variable ocean_proximity
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Z <- onehot_enc(dat[c('ocean_proximity')])
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dat <- cbind(dat, as.data.frame(Z))
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dat <- dat[,!(colnames(dat) %in% c('ocean_proximity'))]
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dat.all <- dat
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# separate observed variables X from the target Y
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X <- dat[,!(colnames(dat) %in% c('median_house_value'))]
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Y <- dat[,c('median_house_value')]
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names(Y) <- rownames(X)
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dat <- list(X = X, Y = Y)
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# split the dataset into train and test
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split <- splitdata(dat, 0.8)
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train <- split$entr
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test <- split$test
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r <- list( dat=dat.all, train=train, test=test )
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return(r)
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}
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# create a categorical dataset for correspondence analysis of the training data
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dataset.housing.mca <-
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function(dat=NULL)
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{
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# loading dataset in memory
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if(is.null(dat)) { dat <- read.csv(file="data/housing.csv", header=TRUE) }
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# transform ocean_proximity into a factor
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dat$ocean_proximity <- as.factor(dat$ocean_proximity)
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="<1H OCEAN"] <- "O:<1H"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="ISLAND"] <- "O:ISL"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="INLAND"] <- "O:INL"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR BAY"] <- "O:NB"
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levels(dat$ocean_proximity)[levels(dat$ocean_proximity)=="NEAR OCEAN"] <- "O:NO"
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# discretization of longitude
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cuts <- kcuts(x = dat$longitude, centers = 4)
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dat$c_longitude <- cut(x = dat$longitude, unique(cuts), include.lowest = TRUE)
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levels(dat$c_longitude) <- c('LO:W', 'LO:MW', 'LO:ME', 'LO:E')
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# discretization of latitude
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cuts <- kcuts(x = dat$latitude, centers = 4)
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dat$c_latitude <- cut(x = dat$latitude, unique(cuts), include.lowest = TRUE)
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levels(dat$c_latitude) <- c('LA:S','LA:MS','LA:MN','LA:N')
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# discretization of housing_median_age
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cuts <- c(min(dat$housing_median_age), 15, 25, 35, 51, 52)
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dat$c_age <- cut(x = dat$housing_median_age, unique(cuts),
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include.lowest = TRUE)
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levels(dat$c_age) <- c('AG:15]','AG:25]','AG:35]','AG:51]', 'AG:52')
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# creation and discretization of rooms
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dat$rooms <- dat$total_rooms / dat$households
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cuts <- c(min(dat$rooms), 4, 6, 8, max(dat$rooms))
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dat$c_rooms <- cut(x = dat$rooms, unique(cuts), include.lowest = TRUE)
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levels(dat$c_rooms) <- c('RO:4]','RO:6]','RO:8]', 'RO:>8')
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# creation and discretization of bedrooms
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dat$bedrooms <- dat$total_bedrooms / dat$households
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cuts <- c(min(dat$bedrooms, na.rm = TRUE), 1.1, max(dat$bedrooms, na.rm = TRUE))
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dat$c_bedrooms <- cut(x = dat$bedrooms, unique(cuts), include.lowest = TRUE)
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levels(dat$c_bedrooms) <- c('BE:1]','BE:>1')
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# creation and discretization of pop
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dat$pop <- dat$population / dat$households
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cuts <- c(min(dat$pop), 2, 3, 4, max(dat$pop))
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dat$c_pop <- cut(x = dat$pop, unique(cuts), include.lowest = TRUE)
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levels(dat$c_pop) <- c('PO:2]','PO:3]', 'PO:4]', 'PO:>4')
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# discretization of households
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cuts <- c(min(dat$households), 300, 400, 600, max(dat$households))
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dat$c_households <- cut(x = dat$households, cuts, include.lowest = TRUE)
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levels(dat$c_households) <- c('HH:3]', 'HH:4]', 'HH:6]', 'HH:>6')
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# discretization of median_income
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cuts <- quantile(dat$median_income, probs = seq(0,1,1/4))
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cuts <- c(cuts[1:length(cuts)-1], 15, max(dat$median_income))
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dat$c_income <- cut(x = dat$median_income, cuts, include.lowest = TRUE)
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levels(dat$c_income) <- c('IC:L', 'IC:ML', 'IC:MH', 'IC:H', 'IC:>15')
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# discretization of median_house_value
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cuts <- c(min(dat$median_house_value), 115000, 175000, 250000,
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max(dat$median_house_value))
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dat$c_house_value <- cut(x = dat$median_house_value, cuts,
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include.lowest = TRUE)
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levels(dat$c_house_value) <- c('HV:A', 'HV:B', 'HV:C', 'HV:D')
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# discretized version of the entire dataset
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dat <- dat[c('ocean_proximity', 'c_longitude', 'c_latitude', 'c_age',
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'c_rooms', 'c_bedrooms', 'c_pop', 'c_households', 'c_income',
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'c_house_value')]
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vent <- list()
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# ventilation of modality RO:>8 of variable c_rooms
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vent$c_rooms <- ventilate(dat$c_rooms, "RO:>8")
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dat$c_rooms[vent$c_rooms$sup_i] <- vent$c_rooms$smpl
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# ventilation of modality IC:>15 of variable c_income
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vent$c_income <- ventilate(dat$c_income, "IC:>15")
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dat$c_income[vent$c_income$sup_i] <- vent$c_income$smpl
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# ventilation of missing values of variable c_bedrooms
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vent$c_bedrooms <- ventilate(dat$c_bedrooms, "NA")
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dat$c_bedrooms[vent$c_bedrooms$sup_i] <- vent$c_bedrooms$smpl
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# removal of empty modalities following the various ventilations
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dat <- droplevels(dat)
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# c_house_value, the target, is considered as a supplementary variable
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supvar <- which(names(dat) == "c_house_value")
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r <- list( vent=vent, dat=dat, supvar=supvar )
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return(r)
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}
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print("load dataset")
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dat <- dataset.housing()
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print("Nystroem approx ridge regression")
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nakrm <- nakr(dat$train$X, dat$train$Y)
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nakrm.yh <- predict(nakrm, dat$test$X)
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nakrm.mae <- mean(abs(nakrm.yh - dat$test$Y))
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print(paste("Test MAE for Nystroem: ", nakrm.mae))
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# correspondence analysis of the training set
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rawdat <- read.csv(file="data/housing.csv", header=TRUE)
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train.rawdat <- rawdat[as.numeric(rownames(dat$train$X)),]
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dat.mca <- dataset.housing.mca(train.rawdat)
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cam <- mca(dat.mca)
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# average of the prediction errors by clusters
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nakrm.yh.train <- predict(nakrm, dat$train$X)
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nakrm.mae.train <- mean(abs(nakrm.yh.train - dat$train$Y))
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nakrm.err.train <- abs(nakrm.yh.train - dat$train$Y)
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clst.err <- aggregate(nakrm.err.train, list(cam$clsti$cluster), FUN=mean)
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clst.err$Group.1 <- paste0("clst-", as.character(clst.err$Group.1))
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clst.tbl <- clstcor.mca(cam)
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clst.err <- clst.err[match(rownames(clst.tbl),clst.err$Group.1),]
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clst.tbl <- cbind(clst.tbl, clst.err[,2])
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colnames(clst.tbl)[ncol(clst.tbl)] <- "ERR"
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clst.tbl <- clst.tbl[order(-clst.tbl[,"ERR"]),]
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print("training set clusters obtained by correspondence analysis and ordered by amount of error")
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print(clst.tbl)
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# From these informations, we could try to understand why for some clusters the model commits greater errors...
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print("Linear ridge regression")
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rm <- ridge(dat$train$X, dat$train$Y)
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rm.yh <- predict(rm, dat$test$X)
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rm.mae <- mean(abs(rm.yh - dat$test$Y))
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print(paste("Test MAE for Ridge: ", rm.mae))
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print("Random forest")
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library(randomForest)
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rfm <- randomForest(dat$train$X, dat$train$Y)
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rfm.yh <- predict(rfm, dat$test$X)
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rfm.mae <- mean(abs(rfm.yh - dat$test$Y))
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print(paste("Test MAE for Random Forest: ", rfm.mae)) |