xgb, xgboost, hyperparameters

xgb_params = {
    'random_state': 1, 
    'n_jobs': 4,
    'booster': 'gbtree',
    'n_estimators': 10000,
    'learning_rate': 0.034682894846408095,
    'reg_lambda': 1.224383455634919,
    'reg_alpha': 36.043214512614476,
    'subsample': 0.9219010649982458,
    'colsample_bytree': 0.11247495917687526,
    'max_depth': 3,
    'min_child_weight': 6,
    'tree_method': 'gpu_hist',
    'gpu_id': 0,
    'predictor': 'gpu_predictor'
}
early_stopping_rounds = 200 
RMSE = 0.7163730825313033

xgb params = {
'learning_rate': 0.07853392035787837, 
'reg_lambda': 1.7549293092194938e-05, 
'reg_alpha': 14.68267919457715, 
'subsample': 0.8031450486786944, 
'colsample_bytree': 0.170759104940733, 
'max_depth': 3
'n_estimators': 5000
                      }
early_stopping_rounds=300 
RMSE = 0.7167068770798828

xgb_params = {
    'random_state': 1, 
    'tree_method': 'gpu_hist', 
    'gpu_id': 0, 
    'predictor': 'gpu_predictor',
    'n_jobs': 4,
    'booster': 'gbtree',
    'n_estimators': 10000,
    'learning_rate': 0.03628302216953097,
    'reg_lambda': 0.0008746338866473539,
    'reg_alpha': 23.13181079976304,
    'subsample': 0.7875490025178415,
    'colsample_bytree': 0.11807135201147481,
    'max_depth': 3
}
RMSE: 0.716423430212675

xgb_params = {
        'learning_rate': 0.15834717111407332,
        'reg_lambda': 0.008347697504479864,
        'reg_alpha': 28.61195680804279,
        'subsample': 0.9996345489574131,
        'colsample_bytree': 0.10330010325726227,
        'max_depth': 2,
        "n_estimators":7000,
        "random_state":42
    }

'n_estimators': 10000,
'lambda': 0.002737255187493384,
'alpha': 7.1401361029365435e-06,
'colsample_bytree': 0.0943116642365347,
'subsample': 0.14136126196670723,
'learning_rate': 0.013736831072935482,
'max_depth': 1,
'min_child_weight': 114

{'n_estimators':2500, 
'max_depth' : 3,
'learning_rate': 0.1/0.13,
'colsample_bytree':0.13/0.11,
'subsample':1/0.99, 
'random_state':1, 
'reg_alpha':25.9987, 
'booster':'gbtree',
'min_child_weight':1.1}

params_xgb = {
'lambda': 0.7044156083795233, 
'alpha': 9.681476940192473, 
'colsample_bytree': 0.3, 
'subsample': 0.8,
'learning_rate': 0.015, 
'max_depth': 3, 
'min_child_weight': 235,
'random_state': 48,
'n_estimators': 30000}

{'tree_method': 'gpu_hist', 
'subsample': 1.0, 
'reg_alpha': 30, 
'random_state': 0, 
'n_estimators': 500, 
'min_child_weight': 3, 
'max_depth': 5, 
'learning_rate': 0.15, 
'gamma': 0.2, 
'colsample_bytree': 0.7,
'n-jobs': -1}
RMSE =  0.719725592318301

{'tree_method': 'gpu_hist', 
'subsample': 0.8, 
'reg_alpha': 20, 
'random_state': 0, 
'n_estimators': 500,
'min_child_weight': 3, 
'max_depth': 5, 
'learning_rate': 0.15, 
'gamma': 0.0, 
'colsample_bytree': 0.5}
RMSE =  0.7199459090974294

n_estimators=2500, 
max_depth = 2,
learning_rate=0.1,
colsample_bytree= 0.5,
subsample=0.8, 
random_state=1,
reg_alpha = 40, 
min_child_weight=5, 
gamma=0.0, 
tree_method = 'gpu_hist'

n_estimators=1375, 
max_depth = 3,
learning_rate=0.14, 
colsample_bytree= 0.5,
subsample=0.99, 
random_state=1, 
reg_alpha = 25.4

n_estimators=1000, 
max_depth = 3,
learning_rate=0.14,
colsample_bytree= 0.5, 
subsample=0.99, 
random_state=1,
reg_alpha = 25.4, 
tree_method = 'gpu_hist'

learning_rate=0.05, 
max_bin= 165, 
max_depth= 5, 
min_child_samples= 150,
min_child_weight= 0.1, 
min_split_gain= 0.0018, 
n_estimators= 41, 
num_leaves= 6, 
reg_alpha= 2.0,
reg_lambda= 2.54, 
objective= 'binary', n_jobs= -1

*("min_child_samples", "min_split_gain", "num_leaves")might not be used.

Source: Various notebooks on kaggle

XGBoost Hyperparameters