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 = F]) 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')[], model = bst) preds <- predict(bst, d_train) rmse <- sqrt(sum(mean((y - preds)^2)))
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