chg params

src/params/model_params.yaml

@@ -1,35 +1,36 @@
 CatBoost:
   ensemble:
     learning_rate: 0.1
-    depth: 8
+    depth: 10
     loss_function: Logloss
     random_seed: 0
     l2_leaf_reg: 3
     subsample: 1
     grow_policy: SymmetricTree  # SymmetricTree or Depthwise or Lossguide
-    bagging_temperature: 0.5
-    random_strength: 3
-    min_data_in_leaf: 15
+    bagging_temperature: 1
+    random_strength: 2
+    min_data_in_leaf: 20
     iterations: 10000
     early_stopping_rounds: 50
     custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"]
     logging_level: 'Silent'  # or 'Verbose', 'Info', 'Debug'
     train_dir: '/tmp'         # avoid write permission issues
+    auto_class_weights: Balanced  # or None, or SqrtBalanced
 
 # lr1e1_d12_l27_ss07_gpLg_bag1_rs5_m5
 
   single_model:
     # in this mode, the model is trained on the entire dataset using the best_iter obtained from cross-validation
     learning_rate: 0.1
-    depth: 8
+    depth: 10
     loss_function: Logloss
     random_seed: 0
     l2_leaf_reg: 3
     subsample: 1
     grow_policy: SymmetricTree  # SymmetricTree or Depthwise or Lossguide
-    bagging_temperature: 0.5
-    random_strength: 3
-    min_data_in_leaf: 15
+    bagging_temperature: 1
+    random_strength: 2
+    min_data_in_leaf: 20
     custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"]
     logging_level: 'Silent'  # or 'Verbose', 'Info', 'Debug'
     train_dir: '/tmp'         # avoid write permission issues