XGBoost Regressor

Classification

Boosting Algorithms

Import Statement

import xgboost as xgb

Model Name

Status 1

Done

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

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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:

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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__.
XGBRegressor does not yet define this method in its implementation.
As a result, XGBoost models do not fully comply with sklearn.base.BaseEstimator in 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:

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! 🚀