# The best fit models according to highest AUC and lowest RMSE are: \
# neglectus\
features <- c("arope", "Shrub", "hurs_coldest", "BIO04", "Forest", "population")\
target <- "outcome"\
params <- list(\
  eta = 0.3,\
  max_depth = 6,\
  min_child_weight = 1,\
  subsample = 0.6,\
  colsample_bytree = 0.6,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
# perfiliewi\
features <- c("arope", "Shrub", "BIO02", "BIO11", "elevation", "Forest")\
target <- "outcome"\ 
params <- list(\
  eta = 0.1,\
  max_depth = 3,\
  min_child_weight = 1,\
  subsample = 0.6,\
  colsample_bytree = 0.6,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
# ariasi \
features <- c("arope", "BIO06", "elevation", "PermanentWater", "BIO02", "Forest")\
target <- "outcome"\
params <- list(\
  eta = 0.3,\
  max_depth = 3,\
  min_child_weight = 1,\
  subsample = 0.6,\
  colsample_bytree = 0.6,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
# perniciosus \
features <- c("BIO06", "arope", "Forest", "elevation", "Grass", "population")\
target <- "outcome"\ 
params <- list(\
  eta = 0.3,\
  max_depth = 9,\
  min_child_weight = 5,\
  subsample = 1,\
  colsample_bytree = 0.8,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
# tobbi \
features <- c("BIO02", "Shrub", "Grass", "BuiltUp", "BIO04", "Crops", "Forest")\
target <- "outcome"\
params <- list(\
  eta = 0.3,\
  max_depth = 3,\
  min_child_weight = 1,\
  subsample = 0.6,\
  colsample_bytree = 0.6,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\

# cl \
features <- c("arope", "BIO01", "elevation", "Shrub", "hurs_driest")\
target <- "outcome"\ 
params <- list(\
  eta = 0.1,\
  max_depth = 9,\
  min_child_weight = 1,\
  subsample = 0.6,\
  colsample_bytree = 0.6,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
# vl \
features <- c("arope", "Shrub", "elevation", "BIO10", "hurs_driest", "BIO04")\
target <- "outcome"\ 
params <- list(\
  eta = 0.1,\
  max_depth = 9,\
  min_child_weight = 5,\
  subsample = 1,\
  colsample_bytree = 1,\
  objective = "binary:logistic",\
  eval_metric = "auc",\
  seed = 42\
)\
\
