From 07c1081ed806dcb34403f29ed7a6858c40951103 Mon Sep 17 00:00:00 2001 From: Vincent Arel-Bundock Date: Fri, 6 Oct 2023 09:19:37 -0400 Subject: [PATCH] website --- docs/articles/machine_learning.html | 125 ++++++++++-------- .../figure-html/unnamed-chunk-15-1.png | Bin 25869 -> 25240 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 36018 -> 36104 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 191702 -> 191172 bytes docs/search.json | 4 +- 5 files changed, 69 insertions(+), 60 deletions(-) diff --git a/docs/articles/machine_learning.html b/docs/articles/machine_learning.html index bd58db858..e5a84d3bc 100644 --- a/docs/articles/machine_learning.html +++ b/docs/articles/machine_learning.html @@ -585,11 +585,11 @@


            Term           Contrast Estimate
  bill_length_mm +1                    0.000
- island         Dream - Biscoe        0.154
- island         Torgersen - Biscoe   -1.900
- sex            male - female         2.724
- species        Chinstrap - Adelie    6.035
- species        Gentoo - Adelie       5.933
+ island         Dream - Biscoe        0.227
+ island         Torgersen - Biscoe   -2.209
+ sex            male - female         2.605
+ species        Chinstrap - Adelie    6.088
+ species        Gentoo - Adelie       6.112
 
 Columns: term, contrast, estimate 
 Type:  numeric 
@@ -645,35 +645,44 @@

library(modelsummary)
 
-recipe <- penguins |>
+# pre-processing
+pre <- penguins |>
     recipe(sex ~ ., data = _) |>
     step_ns(bill_length_mm, deg_free = 4) |>
     step_dummy(all_nominal_predictors())
 
+# modelling strategies
 models <- list(
-  logit = logistic_reg(mode = "classification", engine = "glm"),
-  forest = rand_forest(mode = "classification", engine = "ranger"),
-  xgb = boost_tree(mode = "classification", engine = "xgboost")
+  "Logit" = logistic_reg(mode = "classification", engine = "glm"),
+  "Random Forest" = rand_forest(mode = "classification", engine = "ranger"),
+  "XGBooost" = boost_tree(mode = "classification", engine = "xgboost")
 )
 
-lapply(models, \(x) {
-  recipe |>
+# fit to data
+fits <- lapply(models, \(x) {
+  pre |>
   workflow(spec = x) |>
-  fit(penguins) |>
-  avg_comparisons(newdata = penguins, type = "prob") }) |>
-  modelsummary(
-    shape = term + contrast + group ~ model,
-    coef_omit = "sex",
-    coef_rename = coef_rename)
+ fit(penguins) +}) + +# marginaleffects +cmp <- lapply(fits, avg_comparisons, newdata = penguins, type = "prob") + +# summary table +modelsummary( + cmp, + shape = term + contrast + group ~ model, + coef_omit = "sex", + coef_rename = coef_rename)
- - - + + + @@ -681,7 +690,7 @@

- + @@ -697,7 +706,7 @@

- + @@ -713,7 +722,7 @@

- + @@ -729,7 +738,7 @@

- + @@ -745,7 +754,7 @@

- + @@ -761,7 +770,7 @@

- + @@ -777,7 +786,7 @@

- + @@ -793,7 +802,7 @@

- + @@ -809,7 +818,7 @@

- + @@ -825,7 +834,7 @@

- + @@ -890,37 +899,37 @@


        Term      Contrast Estimate
  count      +1               0.000
- holiday    False - True    12.556
- humidity   +1             -23.124
- month      +1               3.892
- season     spring - fall  -31.058
- season     summer - fall   -8.917
- season     winter - fall    2.506
- temp       +1               3.409
- weather    misty - clear   -7.332
- weather    rain - clear   -58.989
- weekday    Fri - Sun       74.481
- weekday    Mon - Sun       82.925
- weekday    Sat - Sun       23.244
- weekday    Thu - Sun       90.207
- weekday    Tue - Sun       88.818
- weekday    Wed - Sun       91.096
- windspeed  +1               0.221
- workingday False - True  -187.642
- year       1 - 0           99.448
+ holiday    False - True    13.101
+ humidity   +1             -23.369
+ month      +1               3.902
+ season     spring - fall  -29.587
+ season     summer - fall   -8.385
+ season     winter - fall    3.458
+ temp       +1               3.701
+ weather    misty - clear   -7.973
+ weather    rain - clear   -59.693
+ weekday    Fri - Sun       69.059
+ weekday    Mon - Sun       77.247
+ weekday    Sat - Sun       20.396
+ weekday    Thu - Sun       84.326
+ weekday    Tue - Sun       83.109
+ weekday    Wed - Sun       85.338
+ windspeed  +1               0.136
+ workingday False - True  -190.696
+ year       1 - 0           97.664
 
 Columns: term, contrast, estimate 
 Type:  response 
-

These results are easy to interpret: An increase of 1 degree Celsius in the temperature is associated with an increase of 3.409 bikes rented per hour.

+

These results are easy to interpret: An increase of 1 degree Celsius in the temperature is associated with an increase of 3.701 bikes rented per hour.

We could obtain the same result manually as follows:

lo <- transform(bikes, temp = temp - 0.5)
 hi <- transform(bikes, temp = temp + 0.5)
 mean(predict(forest, newdata = hi) - predict(forest, newdata = lo))
-
[1] 3.40943
+
[1] 3.70069

@@ -934,7 +943,7 @@


  Term Contrast Estimate
- temp   custom      2.3
+ temp   custom     2.43
 
 Columns: term, contrast, estimate 
 Type:  response 
@@ -949,7 +958,7 @@

feature = "temp", step.size = 1)$ame

-
[1] 2.30096
+
[1] 2.434684

With marginaleffects::avg_comparisons(), we can also compute the average effect of a simultaneous change in multiple predictors, using the variables and cross arguments. In this example, we see what happens (on average) to the predicted outcome when the temp, season, and weather predictors all change together:

@@ -962,12 +971,12 @@


  Estimate     C: season C: temp    C: weather
-   -34.75 spring - fall      +1 misty - clear
-   -77.35 spring - fall      +1 rain - clear 
-   -12.91 summer - fall      +1 misty - clear
-   -61.90 summer - fall      +1 rain - clear 
-    -1.75 winter - fall      +1 misty - clear
-   -55.39 winter - fall      +1 rain - clear 
+  -33.256 spring - fall      +1 misty - clear
+  -76.451 spring - fall      +1 rain - clear 
+  -12.461 summer - fall      +1 misty - clear
+  -62.248 summer - fall      +1 rain - clear 
+   -0.937 winter - fall      +1 misty - clear
+  -54.656 winter - fall      +1 rain - clear 
 
 Columns: term, contrast_season, contrast_temp, contrast_weather, estimate 
 Type:  response 
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logitforestxgbLogitRandom Forest XGBooost
+1 female −0.101−0.059−0.062 −0.075
male 0.1010.0590.062 0.075
Dream - Biscoe female −0.0440.0110.007 −0.004
male 0.044−0.011−0.007 0.004
Torgersen - Biscoe female 0.015−0.056−0.063 0.008
male −0.0150.0560.063 −0.008
Chinstrap - Adelie female 0.5620.1620.166 0.441
male −0.562−0.162−0.166 −0.441
Gentoo - Adelie female 0.4530.1190.110 0.361
male −0.453−0.119−0.110 −0.361