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"""
AdaBoost Classifier setup.
Features:
- Uses `AdaBoostClassifier` wrapping a weak learner (by default DecisionTreeClassifier).
- Suitable for binary and multi-class tasks (OvR approach).
- Default scoring: 'accuracy'.
"""
from sklearn.ensemble import AdaBoostClassifier
estimator = AdaBoostClassifier(random_state=42)
param_grid = {
'model__n_estimators': [100],
'model__learning_rate': [0.5, 1.0],
'model__algorithm': ['SAMME'],
# Preprocessing params
#'preprocessor__num__imputer__strategy': ['mean','median'],
#'preprocessor__num__scaler__with_mean': [True,False],
#'preprocessor__num__scaler__with_std': [True,False],
}
default_scoring = 'accuracy'
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