ML Frameworks (Scikit-learn, XGBoost)
Machine learning frameworks provide pre-built algorithms and tools for building ML models. Scikit-learn is the go-to for classical ML, while XGBoost dominates tabular data competitions.
Best For: Tabular data, structured datasets, traditional ML tasks. For deep learning, use PyTorch or TensorFlow.
Scikit-learn
The most popular Python library for classical machine learning. Simple, consistent API.
python
Output:
Click "Run Code" to see output
XGBoost
Gradient boosting framework. Wins most Kaggle competitions on tabular data.
✓
Speed
Highly optimized, parallel processing
✓
Accuracy
State-of-the-art on structured data
✓
Regularization
Built-in L1/L2 to prevent overfitting
✓
Missing Values
Handles missing data automatically
Key Takeaway: Use Scikit-learn for classical ML and XGBoost for tabular data competitions. Both have simple APIs and excellent documentation.