'max_depth': 5,
'eta': 0.5,
'gamma': 0.1,
'subsample': 1,
'lambda': 1,
'alpha': 0.35,
'objective': 'reg:squarederror',
'eval_metric': ['mae', 'rmse']
}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 20
# Train model using `xgb.train()`
neptune.create_experiment(name='xgb', tags=['train'], params=params)
xgb.train(params, dtrain, num_round, watchlist, callbacks=[neptune_callback()])
neptune.stop()
# Train model using `xgb.cv()`
neptune.create_experiment(name='xgb', tags=['cv'], params=params)
xgb.cv(params,
dtrain,
num_boost_round=num_round,
nfold=7,
callbacks=[neptune_callback()])
neptune.stop()
# Train model using `sklearn` API
params = {'max_depth': 5,
'eta': 0.5,
'gamma': 0.1,
'subsample': 1,
'lambda': 1,
'alpha': 0.35,
'objective': 'reg:squarederror',
'eval_metric': ['mae', 'rmse']}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 20
# Train model using `xgb.train()`
neptune.create_experiment(name='xgb', tags=['train'], params=params)
xgb.train(params, dtrain, num_round, watchlist,
callbacks=[neptune_callback()])
neptune.stop()
# Train model using `xgb.cv()`
neptune.create_experiment(name='xgb', tags=['cv'], params=params)
xgb.cv(params, dtrain, num_boost_round=num_round, nfold=7,
callbacks=[neptune_callback()])
neptune.stop()
# Train model using `sklearn` API
neptune.create_experiment(name='xgb', tags=['sklearn'], params=params)
reg = xgb.XGBRegressor(**params)