用Python进行Logistic回归图绘制学习曲线和决策边界

Question

我已经用Logistic回归训练了一个数据集。但是，我找不到经过训练的数据的学习曲线和决策边界的任何绘图代码块。我把代码放在下面。那么，我该怎么做呢？

library(mapdeck)
library(sf)
library(sfheaders)
library(data.table) 

## 'roads' is an sf object included in mapdeck
sf_roads <- mapdeck::roads

## you don't need this, I only use it later in the plot so I can subset only 'long' 
## roads, so the gif is below 2mb!
sf_roads$length <- as.numeric( sf::st_length( sf_roads ) )
sf_roads$id <- 1:nrow( sf_roads )  ## for joining the 'length' back on

## grab the road coordinates
dt <- sf::st_coordinates( sf_roads )
dt <- as.data.table( dt )

## st_coordinates returns a L1 'id', which is equivalent to our 'id' above

## Need Z and M attributes
dt[, Z := 0 ]   ## can be actual elevation if you have it
dt[, M := 1:.N, by = .(L1)]   ## this 'M' is your timestamp

## convert back to sf LINESTRING
sf_trips <- sfheaders::sf_linestring(
  obj = dt
  , x = "X"
  , y = "Y"
  , z = "Z"
  , m = "M"
  , linestring_id = "L1"
)

## You don't need this bit, where I join the 'length' back on.
sf_roads$geometry <- NULL

sf_trips <- merge(
  x = sf_trips
  , y = sf_roads
  , by.x = "L1"
  , by.y = "id"
)



mapdeck(
  style = mapdeck_style("dark")
) %>%
  add_trips(
    data = sf_trips[ sf_trips$length > 500, ]  ## only showing 'long' roads
    , stroke_colour = "L1"
    , stroke_width = 100
    , opacity = 0.8
    , animation_speed = 10
    , trail_length = 50
    , palette = 
  )