Python XGBoost is a gradient boosting package that provides an efficient and flexible way to build customized models. XGBClassifier is a classifier that implements the XGBoost algorithms for classification. The set_params() method in the XGBClassifier class allows you to set hyperparameters for the model.
Here are some code examples:
Example 1: Setting the learning rate to 0.1 and max depth to 3
from xgboost import XGBClassifier model = XGBClassifier() model.set_params(learning_rate=0.1, max_depth=3)
Example 2: Setting the number of estimators to 100 and early stopping to true
from xgboost import XGBClassifier model = XGBClassifier() model.set_params(n_estimators=100, early_stopping_rounds=10)
Example 3: Setting the objective function to binary logistic regression and the regularization parameter to 0.1
from xgboost import XGBClassifier model = XGBClassifier() model.set_params(objective='binary:logistic', reg_lambda=0.1)
These code examples demonstrate how to set hyperparameters for an XGBoost classifier using the set_params() method. The XGBoost package is used in each example.
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