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library(xgboost)
library(data.table)
library(cplm)
data(AutoClaim)
# auto insurance dataset analyzed by Yip and Yau (2005)
dt <- data.table(AutoClaim)
# exclude these columns from the model matrix
exclude <- c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_YY')
# retains the missing values
# NOTE: this dataset is comes ready out of the box
options(na.action = 'na.pass')
x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = FALSE])
options(na.action = 'na.omit')
# response
y <- dt[, CLM_AMT5]
d_train <- xgb.DMatrix(data = x, label = y, missing = NA)
# the tweedie_variance_power parameter determines the shape of
# distribution
# - closer to 1 is more poisson like and the mass
# is more concentrated near zero
# - closer to 2 is more gamma like and the mass spreads to the
# the right with less concentration near zero
params <- list(
objective = 'reg:tweedie',
eval_metric = 'rmse',
tweedie_variance_power = 1.4,
max_depth = 6,
eta = 1)
bst <- xgb.train(
data = d_train,
params = params,
maximize = FALSE,
watchlist = list(train = d_train),
nrounds = 20)
var_imp <- xgb.importance(attr(x, 'Dimnames')[[2]], model = bst)
preds <- predict(bst, d_train)
rmse <- sqrt(sum(mean((y - preds) ^ 2)))
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