GBClassifier: Set default loss to 'log_loss'

Orange/classification/gb.py

@@ -25,7 +25,7 @@ class GBClassifier(SklLearner, _FeatureScorerMixin):
     __returns__ = SklModel
 
     def __init__(self,
-                 loss="deviance",
+                 loss="log_loss",
                  learning_rate=0.1,
                  n_estimators=100,
                  subsample=1.0,

conda-recipe/meta.yaml

@@ -40,15 +40,15 @@ requirements:
     - setuptools >=41.0.0
     - numpy >=1.19.5
     - scipy >=1.9
-    - scikit-learn >=1.0.1,<1.2.0
+    - scikit-learn >=1.1.0,<1.2.0
     - bottleneck >=1.3.4
     - chardet >=3.0.2
     - xlrd >=1.2.0
     - xlsxwriter
     - anyqt >=0.1.0
     - pyqt >=5.12,!=5.15.1,<6.0
     - pyqtgraph >=0.12.2,!=0.12.4
-    - joblib >=0.11
+    - joblib >=1.0.0
     - keyring
     - keyrings.alt
     - pip >=18.0

requirements-core.txt

@@ -3,7 +3,7 @@ numpy>=1.19.5
 scipy>=1.9
 # scikit-learn version 1.0.0 includes problematic libomp 12 which breaks xgboost
 # https://github.com/scikit-learn/scikit-learn/pull/21227
-scikit-learn>=1.0.1,<1.2.0
+scikit-learn>=1.1.0,<1.2.0
 bottleneck>=1.3.4
 # Reading Excel files
 xlrd>=1.2.0
@@ -12,7 +12,7 @@ xlsxwriter
 # Encoding detection
 chardet>=3.0.2
 # Multiprocessing abstraction
-joblib>=0.11
+joblib>=1.0.0
 keyring
 keyrings.alt    # for alternative keyring implementations
 setuptools>=41.0.0

tox.ini

@@ -50,12 +50,12 @@ deps =
     oldest: pip==18.0
     oldest: numpy==1.19.5
     oldest: scipy==1.9
-    oldest: scikit-learn==1.0.1
+    oldest: scikit-learn==1.1.0
     oldest: bottleneck==1.3.4
     oldest: xlrd==1.2.0
     # oldest: xlsxwriter
     oldest: chardet==3.0.2
-    oldest: joblib==0.11
+    oldest: joblib==1.0.0
     # oldest: keyring
     # oldest: keyrings.alt
     oldest: setuptools==41.0.0