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  • 1. Supervised learning
    • 1.1. Linear Models
    • 1.2. Linear and Quadratic Discriminant Analysis
    • 1.3. Kernel ridge regression
    • 1.4. Support Vector Machines
    • 1.5. Stochastic Gradient Descent
    • 1.6. Nearest Neighbors
    • 1.7. Gaussian Processes
    • 1.8. Cross decomposition
    • 1.9. Naive Bayes
    • 1.10. Decision Trees
    • 1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking
    • 1.12. Multiclass and multioutput algorithms
    • 1.13. Feature selection
    • 1.14. Semi-supervised learning
    • 1.15. Isotonic regression
    • 1.16. Probability calibration
    • 1.17. Neural network models (supervised)
  • 2. Unsupervised learning
    • 2.1. Gaussian mixture models
    • 2.2. Manifold learning
    • 2.3. Clustering
    • 2.4. Biclustering
    • 2.5. Decomposing signals in components (matrix factorization problems)
    • 2.6. Covariance estimation
    • 2.7. Novelty and Outlier Detection
    • 2.8. Density Estimation
    • 2.9. Neural network models (unsupervised)
  • 3. Model selection and evaluation
    • 3.1. Cross-validation: evaluating estimator performance
    • 3.2. Tuning the hyper-parameters of an estimator
    • 3.3. Tuning the decision threshold for class prediction
    • 3.4. Metrics and scoring: quantifying the quality of predictions
    • 3.5. Validation curves: plotting scores to evaluate models
  • 4. Metadata Routing
  • 5. Inspection
    • 5.1. Partial Dependence and Individual Conditional Expectation plots
    • 5.2. Permutation feature importance
  • 6. Visualizations
  • 7. Dataset transformations
    • 7.1. Pipelines and composite estimators
    • 7.2. Feature extraction
    • 7.3. Preprocessing data
    • 7.4. Imputation of missing values
    • 7.5. Unsupervised dimensionality reduction
    • 7.6. Random Projection
    • 7.7. Kernel Approximation
    • 7.8. Pairwise metrics, Affinities and Kernels
    • 7.9. Transforming the prediction target (y)
  • 8. Dataset loading utilities
    • 8.1. Toy datasets
    • 8.2. Real world datasets
    • 8.3. Generated datasets
    • 8.4. Loading other datasets
  • 9. Computing with scikit-learn
    • 9.1. Strategies to scale computationally: bigger data
    • 9.2. Computational Performance
    • 9.3. Parallelism, resource management, and configuration
  • 10. Model persistence
  • 11. Common pitfalls and recommended practices
  • 12. Data Interoperability
    • 12.1. Pandas/Polars Output for Transformers with set_output
    • 12.2. Array API support (experimental)
  • 13. Choosing the right estimator
  • 14. External Resources, Videos and Talks
  • User Guide
  • 12. Data Interoperability

12. Data Interoperability#

  • 12.1. Pandas/Polars Output for Transformers with set_output
  • 12.2. Array API support (experimental)
    • 12.2.1. Enabling array API support
    • 12.2.2. Example usage
    • 12.2.3. Support for Array API-compatible inputs
    • 12.2.4. Input and output array type handling
    • 12.2.5. Common estimator checks

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11. Common pitfalls and recommended practices

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12.1. Pandas/Polars Output for Transformers with set_output
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