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File size: 1,288 Bytes
8ea1e26 4e913ea 8ea1e26 4e913ea c5ba16c 4e913ea 586d10f 4e913ea c5ba16c 8ea1e26 8db6e3f 117737b 308d08d 8ea1e26 4e913ea 8ea1e26 4e913ea c5ba16c 4e913ea 586d10f 4e913ea c5ba16c 8ea1e26 8db6e3f bb7a6f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | CatBoost:
ensemble:
learning_rate: 0.03
depth: 8
loss_function: Logloss
random_seed: 0
l2_leaf_reg: 10
subsample: 1
grow_policy: Lossguide # SymmetricTree or Depthwise or Lossguide
bagging_temperature: 0.5
random_strength: 0
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
single_model:
# in this mode, the model is trained on the entire dataset using the best_iter obtained from cross-validation
learning_rate: 0.03
depth: 8
loss_function: Logloss
random_seed: 0
l2_leaf_reg: 10
subsample: 1
grow_policy: Lossguide # SymmetricTree or Depthwise or Lossguide
bagging_temperature: 0.5
random_strength: 0
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
auto_class_weights: Balanced # or None, or SqrtBalanced |