Merge pull request #1547 from Genarito/fix-ll-example

Fixed example and reproducibility for Log-likelihood
CamDavidsonPilon · Jul 21, 2023 · 0dfd49c · 0dfd49c
2 parents cf62c60 + 9e4f34e
commit 0dfd49c
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/docs/Survival Regression.rst b/docs/Survival Regression.rst
@@ -1134,15 +1134,15 @@ In this author's opinion, the best way to measure predictive performance is eval
     from lifelines import CoxPHFitter
     from lifelines.datasets import load_rossi
 
-    rossi = load_rossi().sample(frac=1.0)
+    rossi = load_rossi().sample(frac=1.0, random_state=25)  # ensures the reproducibility of the example
     train_rossi = rossi.iloc[:400]
     test_rossi = rossi.iloc[400:]
 
-    cph_l2 = CoxPHFitter(penalizer=0.1, l1_ratio=0.).fit(train_rossi, 'week', 'arrest')
     cph_l1 = CoxPHFitter(penalizer=0.1, l1_ratio=1.).fit(train_rossi, 'week', 'arrest')
+    cph_l2 = CoxPHFitter(penalizer=0.1, l1_ratio=0.).fit(train_rossi, 'week', 'arrest')
 
-    print(cph_l2.score(test_rossi))
-    print(cph_l1.score(test_rossi)) # better model
+    print(cph_l1.score(test_rossi))
+    print(cph_l2.score(test_rossi))  # higher is better
 
 Akaike information criterion (AIC)
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