如何在 tidymodels 中指定虚拟模型/启发式规则作为模型？

Question

我正在使用 tidymodels 和工作流集比较数据集上的一些 ML 模型，并且我想同时将它们与领域中常用的启发式规则进行比较。我认为指定任一规则可能很简单，例如y_pred = (x1 > 3)|(x2 <1) as a model on the same data, tune nothing (as it won't change) and then compare easily to all the other models as it's just a poorly fit model, using yardstick etc. I cannot for the life of me figure out what is the right way to specify it cleanly at the start, the same as the models that actually get fit.

Answer 1

社区贡献的防风草扩展包bespoke允许人们定义这些类型的模型。安装：

pak::pak("macmillancontentscience/bespoke")

主函数

bespoke()

将数据帧作为输入并返回一个向量（整数、字符或因子），指示结果作为输出（每个输入行一个值）。一个简单的例子来说明它的实际效果：

library(parsnip)
library(bespoke)

dat <- data.frame(
  y = factor(sample(c("a", "b"), 10, replace = TRUE)), 
  x1 = rnorm(10), 
  x2 = rnorm(10, .5)
)

make_pred <- function(x) {
  y_pred <- x$x1 > x$x2
  factor(y_pred, labels = c("a", "b"))
}

model_spec <- bespoke(fn = make_pred)

model_spec
#> bespoke Model Specification (classification)
#> 
#> Main Arguments:
#>   fn = make_pred
#> 
#> Computational engine: bespoke

model_fit <- model_spec %>% fit(y ~ x1 + x2, dat)

predict(model_fit, dat)
#> # A tibble: 10 × 1
#>    .pred_class
#>    <fct>      
#>  1 b          
#>  2 b          
#>  3 b          
#>  4 a          
#>  5 a          
#>  6 b          
#>  7 a          
#>  8 b          
#>  9 a          
#> 10 b

^{创建于 2024-03-20，使用 reprex v2.1.0}

:)