import xgboost as xgb
XGBoost Documentation — xgboost 2.1.1 documentation
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
xgboost.readthedocs.io
Introduction
Key Differences Between XGBClassifier and XGBRegressor
Core Features of XGBoost
How XGBoost Works
The Optimized Data Structure in XGBoost: xgb.DMatrix
Complete Parameter Table for XGBoost
Example 1
Example 2
Guide: num_boost_round vs  n_estimators
Best Practices
Example Comparing Both
Summary
Advanced Features
📌 Understanding the Error
The error message:
pgsql
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AttributeError: 'super' object has no attribute '__sklearn_tags__'
occurs because XGBoost (XGBRegressor) does not fully implement sklearn's new API requirements, which were introduced in Scikit-Learn 1.7.
🔍 Why is this happening?
- Scikit-Learn 1.7+ introduced a new method 
__sklearn_tags__. XGBRegressordoes not yet define this method in its implementation.- As a result, XGBoost models do not fully comply with 
sklearn.base.BaseEstimatorin the latest versions of Scikit-Learn. 
✅ Solution: Downgrade Scikit-Learn
Option 1: Use an Earlier Version of Scikit-Learn
Since Scikit-Learn 1.7+ is not fully compatible with XGBoost yet, downgrade it to 1.6.1:
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pip install scikit-learn==1.6.1
Then restart your notebook or script.
✅ Alternative Fixes
Option 2: Use XGBoost’s .fit() Instead of sklearn Integration
Instead of XGBRegressor().fit(), use xgb.train() with DMatrix:
python
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import xgboost as xgb
# Convert to DMatrix
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# Define parameters
params = {
    "objective": "reg:squarederror",
    "eval_metric": "rmse",
    "max_depth": 2,
    "learning_rate": 0.1,
}
# Train XGBoost model
xgb_model = xgb.train(params, dtrain, num_boost_round=100)
This bypasses Scikit-Learn entirely.
🚀 Best Fix?
✅ For compatibility: Use pip install scikit-learn==1.6.1
✅ For XGBoost-native training: Use xgb.train() with DMatrix.
Let me know which approach you prefer! 🚀