Tests updated due to bugfix on the ADWIN change detector - reported o…

…n issue #1614

river/ensemble/streaming_random_patches.py

@@ -407,7 +407,7 @@ class SRPClassifier(BaseSRPEnsemble, base.Classifier):
     >>> metric = metrics.Accuracy()
 
     >>> evaluate.progressive_val_score(dataset, model, metric)
-    Accuracy: 71.97%
+    Accuracy: 72.17%
 
     Notes
     -----

river/forest/adaptive_random_forest.py

@@ -565,7 +565,7 @@ class ARFClassifier(BaseForest, base.Classifier):
     >>> metric = metrics.Accuracy()
 
     >>> evaluate.progressive_val_score(dataset, model, metric)
-    Accuracy: 71.17%
+    Accuracy: 67.97%
 
     The total number of warnings and drifts detected, respectively
     >>> model.n_warnings_detected(), model.n_drifts_detected()
@@ -849,7 +849,7 @@ class ARFRegressor(BaseForest, base.Regressor):
     >>> metric = metrics.MAE()
 
     >>> evaluate.progressive_val_score(dataset, model, metric)
-    MAE: 0.788619
+    MAE: 0.772113
 
     """
 

river/forest/online_extra_trees.py

@@ -614,7 +614,7 @@ class OXTRegressor(ExtraTrees, base.Regressor):
     >>> metric = metrics.RMSE()
 
     >>> evaluate.progressive_val_score(dataset, model, metric)
-    RMSE: 3.127311
+    RMSE: 3.16212
 
     References
     ----------

river/imblearn/chebyshev.py

@@ -162,10 +162,10 @@ class ChebyshevOverSampler(base.Wrapper, base.Regressor):
     ...     metrics.MAE(),
     ...     print_every=500
     ... )
-    [500] MAE: 1.673902
-    [1,000] MAE: 1.743046
-    [1,001] MAE: 1.741335
-    MAE: 1.741335
+    [500] MAE: 1.629786
+    [1,000] MAE: 1.663799
+    [1,001] MAE: 1.66253
+    MAE: 1.66253
 
     References
     ----------

river/tree/hoeffding_adaptive_tree_regressor.py

@@ -140,7 +140,7 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor):
     >>> metric = metrics.MAE()
 
     >>> evaluate.progressive_val_score(dataset, model, metric)
-    MAE: 0.823026
+    MAE: 0.917576
 
     """