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scikit-learn 0.24.2
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User Guide
1. Supervised learning
2. Unsupervised learning
3. Model selection and evaluation
4. Inspection
5. Visualizations
6. Dataset transformations
7. Dataset loading utilities
8. Computing with scikit-learn
8.1. Strategies to scale computationally: bigger data
8.2. Computational Performance
8.3. Parallelism, resource management, and configuration
9. Model persistence
10. Common pitfalls and recommended practices
8.
Computing with scikit-learn
¶
8.1. Strategies to scale computationally: bigger data
8.1.1. Scaling with instances using out-of-core learning
8.2. Computational Performance
8.2.1. Prediction Latency
8.2.2. Prediction Throughput
8.2.3. Tips and Tricks
8.3. Parallelism, resource management, and configuration
8.3.1. Parallelism
8.3.2. Configuration switches
