</>
pythonintermediate

XGBoost with Early Stopping and SHAP

Train an XGBoost model with early stopping and explain predictions using native SHAP integration.

python
import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.1)

dtrain = xgb.DMatrix(X_tr, label=y_tr)
dval   = xgb.DMatrix(X_val, label=y_val)
dtest  = xgb.DMatrix(X_test)

params = {'objective':'binary:logistic', 'max_depth': 5, 'eta': 0.1, 'eval_metric': 'auc'}
model  = xgb.train(params, dtrain, num_boost_round=500, evals=[(dval,'val')], early_stopping_rounds=20, verbose_eval=50)

# SHAP values
shap_values = model.predict(dtest, pred_contribs=True)
print('Feature importance via SHAP:', np.abs(shap_values[:,:-1]).mean(axis=0).argsort()[::-1][:5])

Use Cases

  • classification
  • SHAP explanations
  • early stopping

Tags

#xgboost#early-stopping#shap#gradient-boosting

Related Snippets

Similar patterns you can reuse in the same workflow.

pythonintermediate

SHAP Model Explainability in Python

Explain ML model predictions globally and locally using SHAP values with tree-based models.

Best for: model interpretability

#shap#explainability
pythonintermediate

CatBoost Gradient Boosting Training

Train a CatBoost model with automatic categorical feature handling and built-in cross-validation.

Best for: categorical ML

#catboost#gradient-boosting
pythonintermediate

LightGBM Feature Importance Analysis

Train a LightGBM model and analyse feature importance using split, gain, and permutation methods.

Best for: feature selection

#lightgbm#feature-importance
pythonintermediate

Xgboost Advanced

Data science technique: xgboost-advanced

Best for: machine learning

#data#machine-learning