jtilly

library(xgboost)
set.seed(1234)

N = 1000
x1 <- runif(N)
x <- ifelse(x1 <= 0.2, as.numeric(NA), x1)
y <- as.numeric(x1 >= 0.9)

bst <- xgboost(data = matrix(x, ncol=1), label = y,
               objective = "binary:logistic", eval_metric = "logloss",

jtilly

library(lightgbm)
library(tidyverse)

rm(list = ls())

# We load the default iris dataset shipped with R
data(iris)

iris = as_data_frame(iris) %>% 
    mutate(Species = as.numeric(factor(Species)) - 1) %>% 

jtilly

library(xgboost)
library(dplyr)

params = list(min_child_weight = 0.00001, lambda = 0 )
nrounds = 1

# sparse ---