From 7231073f440e54db2691ec227ae038a9906d850f Mon Sep 17 00:00:00 2001
From: bcjaeger You can also compute variable importance using
@@ -244,9 +242,9 @@ A faster alternative to permutation and negation importance is
@@ -256,9 +254,9 @@ The out-of-bag estimate of Harrell’s C-index (the default method to
-evaluate out-of-bag predictions) is 0.7741875.Oblique RFs for
#> N trees: 500
#> N predictors total: 7
#> N predictors per node: 3
-#> Average leaves per tree: 5.662
+#> Average leaves per tree: 5.674
#> Min observations in leaf: 5
#> OOB stat value: 1.00
#> OOB stat type: AUC-ROC
@@ -158,9 +158,9 @@
Oblique RFs for
#> N trees: 500
#> N predictors total: 7
#> N predictors per node: 3
-#> Average leaves per tree: 49.89
+#> Average leaves per tree: 49.698
#> Min observations in leaf: 5
-#> OOB stat value: 0.81
+#> OOB stat value: 0.82
#> OOB stat type: RSQ
#> Variable importance: anova
#>
@@ -188,7 +188,7 @@
Oblique RFs for
#> Average leaves per tree: 22
#> Min observations in leaf: 5
#> Min events in leaf: 1
-#> OOB stat value: 0.79
+#> OOB stat value: 0.78
#> OOB stat type: Harrell's C-index
#> Variable importance: anova
#>
@@ -226,14 +226,12 @@
Variable importance
+#> bili protime copper age albumin trig
+#> 0.149676551 0.052619056 0.034172481 0.022415623 0.020891770 0.019405707
+#> platelet edema sex ascites chol alk.phos
+#> 0.017796102 0.015097046 0.014810655 0.014618782 0.014366036 0.013015194
+#> stage ast trt spiders hepato
+#> 0.009928079 0.006362569 0.006302797 0.006070745 -0.002828306
orsf_vi_negate(pbc_fit)
-#> bili copper stage age sex
-#> 0.1411713766 0.0489747659 0.0303448559 0.0295340329 0.0278745532
-#> albumin ast spiders platelet trig
-#> 0.0242366278 0.0197760286 0.0134711664 0.0130730519 0.0120909633
-#> edema trt chol ascites alk.phos
-#> 0.0101264568 0.0097735292 0.0078632740 0.0060384900 0.0047323083
-#> hepato protime
-#> -0.0005241144 -0.0009149137
Variable importance
orsf_vi_permute(penguin_fit)
#> bill_length_mm flipper_length_mm bill_depth_mm island
-#> 0.17411531 0.10219725 0.07252006 0.06829739
+#> 0.1738481676 0.0994861528 0.0806801023 0.0624489579
#> body_mass_g sex year
-#> 0.06472311 0.01887897 0.00101881
+#> 0.0619700243 0.0191712699 0.0009713312
Variable importance
orsf_vi_anova(bill_fit)
#> species sex island flipper_length_mm
-#> 0.35164246 0.21178821 0.11241493 0.08642911
+#> 0.35901623 0.21517997 0.11804238 0.08894653
#> body_mass_g bill_depth_mm year
-#> 0.07917470 0.06769970 0.01302696
+#> 0.07557265 0.06381356 0.01412329
Don’t specify a
control
# unspecified control is much faster
time_net['elapsed'] / time_fast['elapsed']
#> elapsed
-#> 42.875
+#> 45.3913
Use
n_thread
@@ -218,11 +218,11 @@ Don’t wait. Estimate! time_est <- orsf_time_to_train(fit_spec, n_tree_subset = 5)
)
#> user system elapsed
-#> 0.312 0.036 0.348
+#> 0.318 0.008 0.325
# the estimated training time:
time_est
-#> Time difference of 138.9851 secs
Out-of-bag predictions and error# what is the output from this function?
fit$eval_oobag$stat_values
#> [,1]
-#> [1,] 0.7741875
+#> [1,] 0.7838308
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