Tidy model willow tit predictions
library(tidyverse); library(mgcv); library(mgcViz); library(janitor); library(tidymodels)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
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## required_pkgs.model_spec parsnip## ── Attaching packages ────────────────────────────────────── tidymodels 0.1.4 ──## ✓ broom 0.7.10 ✓ rsample 0.1.1
## ✓ dials 0.0.10 ✓ tune 0.1.6
## ✓ infer 1.0.0 ✓ workflows 0.2.4
## ✓ modeldata 0.1.1 ✓ workflowsets 0.1.0
## ✓ parsnip 0.1.7 ✓ yardstick 0.0.9
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## • Use suppressPackageStartupMessages() to eliminate package startup messageslibrary(skimr); library(ggmap); library(vip)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vid <- readRDS("~/Dropbox/Mac (2)/Desktop/aru-projects/sd_data.rds")
tidymodels_prefer()
glimpse(d)## Rows: 4,306
## Columns: 11
## $ rID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, …
## $ lc1990 <dbl> 21, 3, 4, 21, 4, 4, 4, 3, 4, 3, 4, 3, 20, 3, 4,…
## $ lc2000 <dbl> 7, 3, 3, 7, 3, 4, 3, 3, 4, 3, 4, 1, 7, 3, 4, 1,…
## $ lc2015 <dbl> 10, 1, 4, 10, 4, 4, 3, 3, 4, 3, 4, 4, 10, 3, 4,…
## $ bt <dbl> 603.0000, 1194.0000, 1251.2693, 575.7488, 269.5…
## $ gsw <dbl> 1072.00000, 1147.00000, 1265.49651, 764.46792, …
## $ meantemp <dbl> 93.43246, 95.09166, 94.10497, 91.58929, 97.1954…
## $ diurnal_range <dbl> 72.99537, 72.97844, 74.00000, 75.80238, 62.0000…
## $ min_temp_coldest_month <dbl> 5.4324588, 8.1833136, -3.1581891, 0.5902241, 15…
## $ presence <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ coords <dbl[,3]> <matrix[26 x 3]>Code book
- lc1990 21 category landcover - dominant class
- lc2000, lc2000 10 category landcover - dominant class per km2
- lc2015, lc2015 10 category landcover - dominant class per km2
- bt blue tit hectad counts (observations per km2)
- gsw great spotted woodpecker hectad counts (observations per km2)
- meantemp mean temp across year C
- diurnal_range mean daily variation C
- min_temp_coldest min temp on coldest day C
- presence presence/ pseudo-absence willow tit (estimated by random sampling background as part of
sdmDatapreparation) - rID row ID
- coords x y coordinates in metres
Convert outcome to factor and extract x, y variavles
d_1 <- d %>%
mutate(presence = factor(presence),
coords1 = data.frame(coords),
x = coords1[, 2],
y = coords1[, 3])
d_1 <- d_1 %>%
select(-c(rID, coords, coords1))
d_1 %>%
head()Split data and create validation dataset
d_split <- initial_split(d_1, strata = presence)
d_train <- training(d_split)
d_test <- testing(d_split)
val_set <- validation_split(d_train,
strata = presence,
prop = 0.80)
val_setCreate recipe (pre-processing)
wt_rec <- recipe(presence ~ bt + gsw + meantemp + diurnal_range + min_temp_coldest_month + lc1990 + lc2000 + lc2015 + x + y, data = d_train) %>%
step_log(bt, base = 10) %>%
step_log(gsw, base = 10) Create mod specs
glm <-
logistic_reg() %>%
set_engine("glm") %>%
set_mode("classification")
lr_mod <-
logistic_reg(penalty = 1) %>%
set_engine("glmnet")
rf_mod <-
rand_forest(mtry = 1, min_n = 8, trees = 1000) %>%
set_engine("ranger", num.threads = 8, importance = "impurity") %>%
set_mode("classification")resamples
wt_cv_folds <- bake(prep(wt_rec),
new_data = d_train) %>%
rsample::vfold_cv(v = 5)Fit models
glmw <- workflow() %>%
add_model(glm) %>%
add_recipe(wt_rec)
glm_fit <- fit(glmw, d_train)
glmnetw <- workflow() %>%
add_model(lr_mod) %>%
add_recipe(wt_rec)
glmnet_fit <- fit(glmnetw, d_train)
rfw <- workflow() %>%
add_model(rf_mod) %>%
add_recipe(wt_rec)
rf_fit <- fit(rfw, d_train)
## after Julia Silge
xgb_spec <- boost_tree(
trees = 1000,
tree_depth = tune(), min_n = tune(),
loss_reduction = tune(), ## first three: model complexity
sample_size = tune(), mtry = tune(), ## randomness
learn_rate = tune(), ## step size
) %>%
set_engine("xgboost") %>%
set_mode("classification")
xgb_spec## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## mtry = tune()
## trees = 1000
## min_n = tune()
## tree_depth = tune()
## learn_rate = tune()
## loss_reduction = tune()
## sample_size = tune()
##
## Computational engine: xgboostxgb_grid <- grid_latin_hypercube(
tree_depth(),
min_n(),
loss_reduction(),
sample_size = sample_prop(),
finalize(mtry(), d_train),
learn_rate(),
size = 30
)
xgb_gridxgboost_wf <- workflow() %>%
add_model(xgb_spec) %>%
add_recipe(wt_rec)
set.seed(42)
vb_folds <- vfold_cv(d_train, strata = presence)
doParallel::registerDoParallel()
set.seed(12345)
xgb_res <- tune_grid(
xgboost_wf,
resamples = vb_folds,
grid = xgb_grid,
control = control_grid(save_pred = TRUE)
)
collect_metrics(xgb_res)xgb_res %>%
collect_metrics() %>%
filter(.metric == "roc_auc") %>%
select(mean, mtry:sample_size) %>%
pivot_longer(mtry:sample_size,
values_to = "value",
names_to = "parameter"
) %>%
ggplot(aes(value, mean, color = parameter)) +
geom_point(alpha = 0.8, show.legend = FALSE) +
facet_wrap(~parameter, scales = "free_x") +
labs(x = NULL, y = "AUC")
show_best(xgb_res, "roc_auc")best_auc <- select_best(xgb_res, "roc_auc")
best_aucfinal_xgb <- finalize_workflow(
xgboost_wf,
best_auc
)
final_xgb## ══ Workflow ════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: boost_tree()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 2 Recipe Steps
##
## • step_log()
## • step_log()
##
## ── Model ───────────────────────────────────────────────────────────────────────
## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## mtry = 3
## trees = 1000
## min_n = 21
## tree_depth = 6
## learn_rate = 0.0044215882579208
## loss_reduction = 2.59086177960244e-08
## sample_size = 0.98803347914014
##
## Computational engine: xgboostfinal_xgb %>%
fit(data = d_train) %>%
pull_workflow_fit() %>%
vip(geom = "point")## Warning: `pull_workflow_fit()` was deprecated in workflows 0.2.3.
## Please use `extract_fit_parsnip()` instead.## [15:37:03] WARNING: amalgamation/../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
final_res <- last_fit(final_xgb, d_split)
collect_metrics(final_res)final_res %>%
collect_predictions() %>%
roc_curve(truth = presence, .pred_0) %>%
autoplot()
Review results
glm
glm_fit %>%
extract_fit_parsnip() %>%
tidy() %>%
filter(p.value < 0.05)• Blue tit density and land cover in 2000 significant predictors
glmnet
glmnet_fit %>%
extract_fit_parsnip() ## parsnip model object
##
## Fit time: 63ms
## $a0
## s0 s1 s2 s3 s4 s5 s6 s7
## -2.572646 -2.820915 -3.064575 -3.303484 -3.537358 -3.765813 -3.988407 -4.204674
## s8 s9 s10 s11 s12 s13 s14 s15
## -4.414151 -4.616405 -4.811051 -4.997763 -5.175442 -5.303299 -5.408145 -5.510894
## s16 s17 s18 s19 s20 s21 s22 s23
## -5.610139 -5.703714 -5.795258 -5.889259 -5.979184 -6.074934 -6.166247 -6.251581
## s24 s25 s26 s27 s28 s29 s30 s31
## -6.331330 -6.405795 -6.475219 -6.539506 -6.598720 -6.653076 -6.703301 -6.802217
## s32 s33 s34 s35 s36 s37 s38 s39
## -6.908797 -7.007100 -7.113132 -7.217797 -7.277613 -7.296324 -7.314345 -7.331853
## s40 s41 s42 s43 s44 s45 s46 s47
## -7.348369 -7.364224 -7.339945 -7.010104 -6.685123 -6.389045 -6.119222 -5.869561
## s48 s49 s50 s51 s52 s53 s54 s55
## -5.648676 -5.441223 -5.254611 -5.085001 -4.930600 -4.790036 -4.658298 -4.540853
## s56 s57 s58 s59 s60 s61 s62 s63
## -4.434563 -4.338081 -4.250499 -4.167098 -4.093577 -4.027623 -3.964261 -3.908951
## s64 s65 s66
## -3.859773 -3.811848 -3.770564
##
## $beta
## 10 x 67 sparse Matrix of class "dgCMatrix"## [[ suppressing 67 column names 's0', 's1', 's2' ... ]]##
## bt . 0.1089267 0.2138032 0.314877 0.4122959 0.5061359
## gsw . . . . . .
## meantemp . . . . . .
## diurnal_range . . . . . .
## min_temp_coldest_month . . . . . .
## lc1990 . . . . . .
## lc2000 . . . . . .
## lc2015 . . . . . .
## x . . . . . .
## y . . . . . .
##
## bt 0.5964235 0.6831537 0.7663051 0.8458513 0.9217698
## gsw . . . . .
## meantemp . . . . .
## diurnal_range . . . . .
## min_temp_coldest_month . . . . .
## lc1990 . . . . .
## lc2000 . . . . .
## lc2015 . . . . .
## x . . . . .
## y . . . . .
##
## bt 0.9940488 1.05151674 1.105254633 1.15927244
## gsw . 0.01112538 0.018948505 0.02210233
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . .
## lc1990 . . . .
## lc2000 . . -0.007446317 -0.01786469
## lc2015 . . . .
## x . . . .
## y . . . .
##
## bt 1.21156181 1.26063355 1.30591236 1.34887580
## gsw 0.02485489 0.02822342 0.03199309 0.03628378
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . .
## lc1990 . . . .
## lc2000 -0.02774801 -0.03707173 -0.04580624 -0.05405815
## lc2015 . . . .
## x . . . .
## y . . . .
##
## bt 1.386285e+00 1.421601e+00 1.456224e+00 1.487329e+00
## gsw 3.935482e-02 4.234712e-02 4.734196e-02 5.379790e-02
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . .
## lc1990 . . -1.642412e-03 -3.420672e-03
## lc2000 -6.142002e-02 -6.832315e-02 -7.235849e-02 -7.571371e-02
## lc2015 . . . .
## x 3.934227e-08 7.694969e-08 1.116772e-07 1.431861e-07
## y . . . .
##
## bt 1.515422e+00 1.541135e+00 1.564773e+00 1.586521e+00
## gsw 6.069014e-02 6.757914e-02 7.430352e-02 8.079491e-02
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . .
## lc1990 -5.069183e-03 -6.593311e-03 -8.000694e-03 -9.298977e-03
## lc2000 -7.881051e-02 -8.167838e-02 -8.433543e-02 -8.679570e-02
## lc2015 . . . .
## x 1.720177e-07 1.984002e-07 2.225375e-07 2.446237e-07
## y . . . .
##
## bt 1.606554e+00 1.625242e+00 1.641857e+00 1.656955e+00
## gsw 8.702133e-02 9.359476e-02 1.001154e-01 1.063439e-01
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . .
## lc1990 -1.046556e-02 -1.098328e-02 -1.144120e-02 -1.186520e-02
## lc2000 -8.905893e-02 -9.071263e-02 -9.224882e-02 -9.366238e-02
## lc2015 -1.415562e-04 -2.001251e-03 -3.729451e-03 -5.309295e-03
## x 2.647471e-07 2.820209e-07 2.981163e-07 3.128514e-07
## y . . . .
##
## bt 1.667107e+00 1.674874e+00 1.681619e+00 1.678396e+00
## gsw 1.252937e-01 1.468891e-01 1.670778e-01 1.930135e-01
## meantemp . . . .
## diurnal_range . . . .
## min_temp_coldest_month . . . 1.367366e-03
## lc1990 -1.241712e-02 -1.303804e-02 -1.360878e-02 -1.413593e-02
## lc2000 -9.453158e-02 -9.520020e-02 -9.582464e-02 -9.715198e-02
## lc2015 -6.937649e-03 -8.353418e-03 -9.634085e-03 -1.068705e-02
## x 3.416055e-07 3.730504e-07 4.027579e-07 4.465784e-07
## y 6.254159e-08 1.385766e-07 2.083233e-07 3.136534e-07
##
## bt 1.671486e+00 1.665014e+00 1.658995e+00 1.653111e+00
## gsw 2.201478e-01 2.474515e-01 2.748337e-01 3.003151e-01
## meantemp . . . .
## diurnal_range . -6.538100e-04 -1.900201e-03 -3.029347e-03
## min_temp_coldest_month 3.201141e-03 4.735416e-03 5.985319e-03 7.135330e-03
## lc1990 -1.462828e-02 -1.505695e-02 -1.542569e-02 -1.576633e-02
## lc2000 -9.870713e-02 -1.001887e-01 -1.015975e-01 -1.029027e-01
## lc2015 -1.158433e-02 -1.236920e-02 -1.305017e-02 -1.365749e-02
## x 4.939074e-07 5.490421e-07 6.105436e-07 6.663775e-07
## y 4.274352e-07 5.297671e-07 6.214288e-07 7.053349e-07
##
## bt 1.648312e+00 1.643317e+00 1.639298e+00 1.636597e+00
## gsw 3.233000e-01 3.448584e-01 3.642364e-01 3.821765e-01
## meantemp . . . -5.869640e-04
## diurnal_range -4.053641e-03 -4.982266e-03 -5.825490e-03 -6.493356e-03
## min_temp_coldest_month 8.184524e-03 9.151927e-03 1.003322e-02 1.120039e-02
## lc1990 -1.608017e-02 -1.636936e-02 -1.663516e-02 -1.688057e-02
## lc2000 -1.041291e-01 -1.052532e-01 -1.063059e-01 -1.072206e-01
## lc2015 -1.419610e-02 -1.467939e-02 -1.510953e-02 -1.553834e-02
## x 7.168956e-07 7.629533e-07 8.046415e-07 8.566219e-07
## y 7.817139e-07 8.517339e-07 9.154371e-07 9.668083e-07
##
## bt 1.634025e+00 1.636682e+00 1.639453e+00 1.642017e+00
## gsw 4.026537e-01 4.177591e-01 4.313874e-01 4.438764e-01
## meantemp -5.758206e-03 -1.087362e-02 -1.553809e-02 -1.979062e-02
## diurnal_range -6.165959e-03 -5.774738e-03 -5.419614e-03 -5.097346e-03
## min_temp_coldest_month 1.529201e-02 1.925389e-02 2.286530e-02 2.615909e-02
## lc1990 -1.708962e-02 -1.727451e-02 -1.744348e-02 -1.759795e-02
## lc2000 -1.077020e-01 -1.082665e-01 -1.088023e-01 -1.093027e-01
## lc2015 -1.617229e-02 -1.673690e-02 -1.724848e-02 -1.771294e-02
## x 1.021890e-06 1.181574e-06 1.326766e-06 1.458877e-06
## y 9.628811e-07 9.525892e-07 9.429517e-07 9.340709e-07
##
## bt 1.644310e+00 1.646561e+00 1.648470e+00 1.650309e+00
## gsw 4.553592e-01 4.658003e-01 4.754569e-01 4.842437e-01
## meantemp -2.372526e-02 -2.721040e-02 -3.048175e-02 -3.342658e-02
## diurnal_range -4.789941e-03 -4.537124e-03 -4.283233e-03 -4.062309e-03
## min_temp_coldest_month 2.920398e-02 3.190864e-02 3.444229e-02 3.672590e-02
## lc1990 -1.773826e-02 -1.786810e-02 -1.798531e-02 -1.809301e-02
## lc2000 -1.097686e-01 -1.102024e-01 -1.106034e-01 -1.109755e-01
## lc2015 -1.813585e-02 -1.851821e-02 -1.886756e-02 -1.918438e-02
## x 1.580702e-06 1.688771e-06 1.789759e-06 1.880658e-06
## y 9.252864e-07 9.183008e-07 9.109015e-07 9.045028e-07
##
## bt 1.651996e+00 1.653545e+00 1.654969e+00 1.656174e+00
## gsw 4.922870e-01 4.996444e-01 5.063707e-01 5.125817e-01
## meantemp -3.610393e-02 -3.854188e-02 -4.076192e-02 -4.284137e-02
## diurnal_range -3.863459e-03 -3.683473e-03 -3.520489e-03 -3.357838e-03
## min_temp_coldest_month 3.880328e-02 4.069563e-02 4.241944e-02 4.403105e-02
## lc1990 -1.819151e-02 -1.828152e-02 -1.836376e-02 -1.843810e-02
## lc2000 -1.113192e-01 -1.116363e-01 -1.119286e-01 -1.121975e-01
## lc2015 -1.947242e-02 -1.973438e-02 -1.997271e-02 -2.019049e-02
## x 1.963231e-06 2.038349e-06 2.106693e-06 2.170477e-06
## y 8.987223e-07 8.934600e-07 8.886699e-07 8.837013e-07
##
## bt 1.657380e+00 1.658486e+00 1.659501e+00 1.660433e+00
## gsw 5.181929e-01 5.233200e-01 5.280027e-01 5.322762e-01
## meantemp -4.469695e-02 -4.637689e-02 -4.790230e-02 -4.928752e-02
## diurnal_range -3.220313e-03 -3.098146e-03 -2.988446e-03 -2.889826e-03
## min_temp_coldest_month 4.547161e-02 4.677698e-02 4.796302e-02 4.904077e-02
## lc1990 -1.850651e-02 -1.856911e-02 -1.862632e-02 -1.867859e-02
## lc2000 -1.124455e-01 -1.126731e-01 -1.128818e-01 -1.130732e-01
## lc2015 -2.038802e-02 -2.056770e-02 -2.073124e-02 -2.088012e-02
## x 2.227461e-06 2.279054e-06 2.325892e-06 2.368419e-06
## y 8.795694e-07 8.759235e-07 8.726609e-07 8.697423e-07
##
## bt 1.661167e+00 1.661952e+00 1.662678e+00 1.663221e+00
## gsw 5.362575e-01 5.398122e-01 5.430499e-01 5.460921e-01
## meantemp -5.060464e-02 -5.176780e-02 -5.281208e-02 -5.381286e-02
## diurnal_range -2.786225e-03 -2.701169e-03 -2.627478e-03 -2.548508e-03
## min_temp_coldest_month 5.006216e-02 5.096659e-02 5.178014e-02 5.255636e-02
## lc1990 -1.872565e-02 -1.876898e-02 -1.880873e-02 -1.884446e-02
## lc2000 -1.132484e-01 -1.134097e-01 -1.135570e-01 -1.136916e-01
## lc2015 -2.101631e-02 -2.113998e-02 -2.125237e-02 -2.135520e-02
## x 2.408664e-06 2.444289e-06 2.476317e-06 2.506846e-06
## y 8.664966e-07 8.639274e-07 8.617671e-07 8.592635e-07
##
## bt 1.663822e+00 1.664379e+00 1.664778e+00 1.665226e+00
## gsw 5.487864e-01 5.512324e-01 5.535578e-01 5.555942e-01
## meantemp -5.468902e-02 -5.546924e-02 -5.622622e-02 -5.688184e-02
## diurnal_range -2.484707e-03 -2.430575e-03 -2.371278e-03 -2.323448e-03
## min_temp_coldest_month 5.323886e-02 5.384864e-02 5.443603e-02 5.494880e-02
## lc1990 -1.887743e-02 -1.890779e-02 -1.893495e-02 -1.896015e-02
## lc2000 -1.138152e-01 -1.139280e-01 -1.140308e-01 -1.141253e-01
## lc2015 -2.144853e-02 -2.153310e-02 -2.161073e-02 -2.168077e-02
## x 2.533667e-06 2.557611e-06 2.580686e-06 2.600775e-06
## y 8.573790e-07 8.558787e-07 8.539881e-07 8.526785e-07
##
## $df
## [1] 0 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 4 4 5 5 5 5
## [26] 5 5 6 6 6 6 7 7 7 8 8 9 9 9 9 9 9 10 10 10 10 10 10 10 10
## [51] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
##
## $dim
## [1] 10 67
##
## $lambda
## [1] 0.0557707238 0.0508162064 0.0463018347 0.0421885073 0.0384405966
## [6] 0.0350256400 0.0319140586 0.0290789016 0.0264956122 0.0241418150
## [11] 0.0219971227 0.0200429589 0.0182623977 0.0166400166 0.0151617634
## [16] 0.0138148341 0.0125875624 0.0114693180 0.0104504154 0.0095220293
## [21] 0.0086761185 0.0079053560 0.0072030660 0.0065631655 0.0059801119
## [26] 0.0054488553 0.0049647940 0.0045237354 0.0041218592 0.0037556846
## [31] 0.0034220399 0.0031180353 0.0028410377 0.0025886477 0.0023586794
## [36] 0.0021491408 0.0019582171 0.0017842544 0.0016257462 0.0014813194
## [41] 0.0013497231 0.0012298174 0.0011205638 0.0010210160 0.0009303117
## [46] 0.0008476654 0.0007723612 0.0007037467 0.0006412278 0.0005842629
## [51] 0.0005323586 0.0004850653 0.0004419735 0.0004027097 0.0003669341
## [56] 0.0003343367 0.0003046352 0.0002775722 0.0002529135 0.0002304453
## [61] 0.0002099732 0.0001913198 0.0001743234 0.0001588370 0.0001447264
## [66] 0.0001318693 0.0001201544
##
## $dev.ratio
## [1] 3.512655e-14 1.606975e-02 3.016732e-02 4.254671e-02 5.341850e-02
## [6] 6.296062e-02 7.132600e-02 7.864803e-02 8.504441e-02 9.061998e-02
## [11] 9.546868e-02 9.967494e-02 1.034056e-01 1.075584e-01 1.115405e-01
## [16] 1.150142e-01 1.180233e-01 1.205939e-01 1.228391e-01 1.248027e-01
## [21] 1.264928e-01 1.281047e-01 1.294983e-01 1.306789e-01 1.316785e-01
## [26] 1.325243e-01 1.332391e-01 1.338454e-01 1.343724e-01 1.348138e-01
## [31] 1.351844e-01 1.358187e-01 1.364410e-01 1.369626e-01 1.376821e-01
## [36] 1.383996e-01 1.390362e-01 1.396010e-01 1.400723e-01 1.404648e-01
## [41] 1.407924e-01 1.410650e-01 1.413446e-01 1.419726e-01 1.425247e-01
## [46] 1.429840e-01 1.433661e-01 1.436872e-01 1.439490e-01 1.441715e-01
## [51] 1.443548e-01 1.445071e-01 1.446338e-01 1.447392e-01 1.448286e-01
## [56] 1.449019e-01 1.449626e-01 1.450131e-01 1.450550e-01 1.450910e-01
## [61] 1.451204e-01 1.451446e-01 1.451655e-01 1.451824e-01 1.451963e-01
## [66] 1.452085e-01 1.452183e-01
##
## $nulldev
## [1] 1653.323
##
## $npasses
## [1] 903
##
## $jerr
## [1] 0
##
## $offset
## [1] FALSE
##
## $classnames
## [1] "0" "1"
##
## $call
## glmnet::glmnet(x = maybe_matrix(x), y = y, family = "binomial")
##
## $nobs
## [1] 3229
##
## attr(,"class")
## [1] "lognet" "glmnet"random forest (ranger)
rf_fit %>%
extract_fit_parsnip() %>%
vip()
xgboost
final_xgb %>%
fit(data = d_train) %>%
pull_workflow_fit() %>%
vip(geom = "point")## Warning: `pull_workflow_fit()` was deprecated in workflows 0.2.3.
## Please use `extract_fit_parsnip()` instead.## [15:37:34] WARNING: amalgamation/../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.