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June 18, 2019 18:53
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#raw data | |
# https://www.fueleconomy.gov/feg/epadata/19data.zip | |
library(gbm) | |
library(caret) | |
r <- function(data) {round(data, 1)} | |
set.seed(123) | |
indices <- sample(nrow(cars_19), size = .75 * nrow(cars_19)) | |
train <- cars_19[indices, ] | |
test <- cars_19[-indices, ] | |
names <- cars[-indices, c(3, 4)] | |
title <- "Gradient Boosted Model" | |
trees <- 1200 | |
m_boosted_reg_untuned <- gbm( | |
formula = fuel_economy_combined ~ ., | |
data = train, | |
n.trees = trees, | |
distribution = "gaussian" | |
) | |
pred_boosted_reg_untuned <- predict(m_boosted_reg_untuned,n.trees=trees, newdata = test) | |
mse_boosted_reg_untuned <- RMSE(pred = pred_boosted_reg_untuned, obs = test$fuel_economy_combined) ^2 | |
boosted_stats_untuned<-postResample(pred_boosted_reg_untuned,test$fuel_economy_combined) | |
#create hyperparameter grid | |
hyper_grid <- expand.grid( | |
shrinkage = seq(.07, .12, .01), | |
interaction.depth = 1:7, | |
optimal_trees = 0, | |
min_RMSE = 0 | |
) | |
#grid search | |
for(i in 1:nrow(hyper_grid)) { | |
set.seed(123) | |
gbm.tune <- gbm( | |
formula = fuel_economy_combined ~ ., | |
data = train_random, | |
distribution = "gaussian", | |
n.trees = 5000, | |
interaction.depth = hyper_grid$interaction.depth[i], | |
shrinkage = hyper_grid$shrinkage[i], | |
) | |
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$train.error) | |
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$train.error)) | |
cat(i,"\n") | |
} | |
m_boosted_reg <- gbm( | |
formula = fuel_economy_combined ~ ., | |
data = train, | |
n.trees = trees, | |
distribution = "gaussian", | |
shrinkage = .09, | |
cv.folds = 5, | |
interaction.depth = 5 | |
) | |
best.iter <- gbm.perf(m_boosted_reg, method = "cv") | |
plot(1:trees,m_boosted_reg$train.error,type="l",ylab="mse",xlab = "number of trees", main = title) | |
which.min(m_boosted_reg$train.error) | |
which.min(m_boosted_reg$cv.error) | |
m_boosted_reg | |
summary(m_boosted_reg) | |
pred_boosted_reg <- predict(m_boosted_reg,n.trees=1183, newdata = test) | |
mse_boosted_reg <- RMSE(pred = pred_boosted_reg, obs = test$fuel_economy_combined) ^2 | |
boosted_stats<-postResample(pred_boosted_reg,test$fuel_economy_combined) | |
tmp <- data.frame(names, test$fuel_economy_combined, r(pred_boosted_reg)) | |
tmp <- tmp[order(tmp$Division, tmp$Carline),] | |
names(tmp)[3] <- "fuel_economy_combined" | |
names(tmp)[4] <- "pred_boosted_reg" | |
res <- r(tmp$fuel_economy_combined - tmp$pred_boosted_reg) | |
tmp[which(abs(res) > boosted_stats[1] * 3), ] #what cars are 3 se residuals | |
plot(res, ylab = "residuals", main = title) | |
plot( | |
test$fuel_economy_combined, | |
pred_boosted_reg_full, | |
main = title, | |
ylab = "predicted", | |
xlab = "actual" | |
) | |
abline(lm(pred_boosted_reg_full ~ test$fuel_economy_combined)) | |
par(mar=c(5,9,5,1)) | |
summary(m_boosted_reg,cBars=11,method=relative.influence,las=2,xlim=c(0,50)) |
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