def get_model(PARAMS):
model = XGBClassifier(booster='gbtree',
silent=True,
nthread=None,
random_state=42,
base_score=0.5,
colsample_bylevel=1,
n_estimators=50,
reg_lambda=1,
objective='binary:logistic')
model.max_depth = PARAMS.get("max_depth")
model.min_child_weight = PARAMS.get("min_child_weight")
model.gamma = PARAMS.get("gamma")
model.subsample = PARAMS.get("subsample")
model.colsample_bytree = PARAMS.get("colsample_bytree")
model.reg_alpha = PARAMS.get('reg_alpha')
model.learning_rate = PARAMS.get("learning_rate")
return model
