Classification
Boosting Algorithms
Import Statement
import xgboost as xgb
Model Name
Status 1
Done
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Introduction
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Key Differences Between XGBClassifier and XGBRegressor
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Core Features of XGBoost
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How XGBoost Works
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The Optimized Data Structure in XGBoost: xgb.DMatrix
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Complete Parameter Table for XGBoost
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Example 1
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Example 2
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Guide: num_boost_round vs n_estimators
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Best Practices
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Example Comparing Both
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Summary
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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:
bash
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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:
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! 🚀