fet_Scikit_Learn
Pages
Home
A. fet_FACULTY
1. fet_Language_&_Linguistics
2. fet_Literature
3. fet_Culture
4. fet_Science
5. fet_Technology
fet_Artificial_Intelligence
fet_Computer_Science_&_Engineering
fet_Data_Science
fet_Electrical_&_Electronics_Engineering
fet_Machine_Learning
fet_Scikit-learn.org
fet_Scikit-learn_Getting_Started
1. Supervised learning
2. Unsupervised learning
4. Inspection
5. Visualizations
6. Dataset transformations
7. Dataset loading utilities
8. Computing with scikit-learn
Pages
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. Ensemble methods
1.12. Multiclass and multilabel algorithms
1.13. Feature selection
1.14. Semi-Supervised
1.15. Isotonic regression
1.16. Probability calibration
1.17. Neural network models (supervised)
1.16. Probability calibration
1.16. Probability calibration
1.16.1. Calibration curves
1.16.2. Calibrating a classifier
1.16.3. Usage
No comments:
Post a Comment
Home
Subscribe to:
Posts (Atom)
No comments:
Post a Comment