AABoyles comments on D&D.Sci

[AABoyles]

Fun! I wish I had a lot more time to spend on this, but here’s a brief and simple basis for a decision:

library(readr)
library(dplyr)
library(magrittr)

training <- read_csv("https://​​raw.githubusercontent.com/​​H-B-P/​​d-and-d-sci/​​main/​​d_and_d_sci.csv″)

training %<>%
  dplyr::mutate(outcome = ifelse(result==”succeed”, 1, 0))

model ← glm(outcome ~ cha + con + dex + int + str + wis, data = training, family = “binomial”)

summary(model)

start ← data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(12), cha = c(4))
predict.glm(model, start, type=”response”)
# > 0.3701247

wise ← data.frame(str = c(6), con = c(15), dex = c(13), int = c(13), wis = c(20), cha = c(5))
predict.glm(model, wise, type=”response”)
# > 0.7314005

charismatic ← data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(12), cha = c(14))
predict.glm(model, charismatic, type=”response”)
# > 0.6510629

wiseAndCharismatic ← data.frame(str = c(6), con = c(14), dex = c(13), int = c(13), wis = c(20), cha = c(6))
predict.glm(model, wiseAndCharismatic, type=”response”)
# > 0.73198

Gonna go with wiseAndCharismatic (+8 Wisdom, +2 Charisma).

[AABoyles]

If I wasn’t trying to not-spend-time-on-this, I would fit a Random Forest or a Neural Network (rather than a logistic regression) to capture some non-linear signal, and, when it predicted well, fire up an optimizer to see how much in which stats really helps.