Scikit-learn 0.22.1 User Guide
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4. Inspection
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2020-03-01 22:50:41
4. Inspection
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3. Model selection and evaluation
3.5. Validation curves: plotting scores to evaluate models
3.3. Metrics and scoring: quantifying the quality of predictions
3.4. Model persistence
3.2. Tuning the hyper-parameters of an estimator
3.1. Cross-validation: evaluating estimator performance
7. Dataset loading utilities
6. Dataset transformations
6.5. Unsupervised dimensionality reduction
6.3. Preprocessing data
6.7. Kernel Approximation
6.8. Pairwise metrics, Affinities and Kernels
6.1. Pipelines and composite estimators
6.9. Transforming the prediction target (y)
6.4. Imputation of missing values
6.2. Feature extraction
6.6. Random Projection
5. Visualizations
4. Inspection
4.2. Permutation feature importance
4.1. Partial dependence plots
1. Supervised learning
1.17. Neural network models (supervised)
1.10. Decision Trees
1.2. Linear and Quadratic Discriminant Analysis
1.5. Stochastic Gradient Descent
1.3. Kernel ridge regression
1.15. Isotonic regression
1.9. Naive Bayes
1.11. Ensemble methods
1.7. Gaussian Processes
1.13. Feature selection
1.14. Semi-Supervised
1.4. Support Vector Machines
1.16. Probability calibration
1.6. Nearest Neighbors
1.12. Multiclass and multilabel algorithms
1.1. Linear Models
1.8. Cross decomposition
2. Unsupervised learning
2.3. Clustering
2.9. Neural network models (unsupervised)
2.5. Decomposing signals in components (matrix factorization problems)
2.4. Biclustering
2.7. Novelty and Outlier Detection
2.2. Manifold learning
2.8. Density Estimation
2.6. Covariance estimation
2.1. Gaussian mixture models
8. Computing with scikit-learn
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