10 Complicated XGBoost Hyperparameters and Find out how to Tune Them Like a Professional in 2023 | by Bex T. | Jun, 2023

1. num_boost_roundn_estimators

Afterwards, it’s a must to decide the variety of resolution bushes (usually known as base learners in XGBoost) to plant throughout coaching utilizing num_boost_round. The default is 100 however that is hardly sufficient for right this moment’s giant datasets.

Rising the parameter will plant extra bushes however considerably will increase the probabilities of overfitting because the mannequin turns into extra complicated.

One trick I discovered from Kaggle is to set a excessive quantity like 100,000 for num_boost_round and make use of early stopping rounds.

In every boosting spherical, XGBoost vegetation yet one more resolution tree to enhance the collective rating of the earlier ones. That’s why it’s known as boosting. This course of continues till num_boost_round rounds, regardless whether or not every new spherical is an enchancment on the final or not.

However by utilizing early stopping, we will cease the coaching and thus planting of pointless bushes when the rating hasn’t been bettering for the final 5, 10, 50 or any arbitrary variety of rounds.

With this trick, we will discover the right variety of resolution bushes with out even tuning num_boost_round and we are going to save time and computation sources. Right here is how it will appear to be in code:

# Outline the remainder of the params
params = {...}

# Construct the practice/validation units
dtrain_final = xgb.DMatrix(X_train, label=y_train)
dvalid_final = xgb.DMatrix(X_valid, label=y_valid)

bst_final = xgb.practice(
params,
dtrain_final,
num_boost_round=100000 # Set a excessive quantity
evals=[(dvalid_final, "validation")],
early_stopping_rounds=50, # Allow early stopping
verbose_eval=False,
)

The above code would’ve made XGBoost use 100k resolution bushes however due to early stopping, it should cease when the validation rating hasn’t been bettering for the final 50 rounds. Often, the variety of required bushes can be lower than 5000–10000.

Controlling num_boost_round can also be one of many greatest components in how lengthy the coaching course of runs as extra bushes require extra sources.

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