Other Model Types

AdvancedAnalytics trains thirty-one model families (Jube.Service/Repository/AnalyticsTrainingRun/, one AdvancedAnalytics*TrainingService.cs per family); six are covered elsewhere in this guide (linear regression, decision trees, gradient boosting, naive Bayes, tree-augmented naive Bayes, neural networks). Logistic regression is covered elsewhere too, but reaches the shared, generic AdvancedAnalyticsTrainingService through its own gateway rather than a dedicated per-family training service of its own (see LogisticRegression.md), so it is not one of these thirty-one. The remaining twenty-two each have their own file in this section, listed in the menu grouped by kind. Every file follows the same shape: the R snippet using the closest common CRAN package, the real training service that replaces it, and a code audit of the actual implementation behind that service (not just its DTO) — what algorithm it really runs, its real defaults, and any divergence from the textbook method, cited to file and line in the reference worktree.

Every family shares the same submission shape: SubmitTrainingAsync on its training service queues a background run and returns a run id at once, the caller polls GetRunAsync for status, and every trained version is immutable and never made live by training itself — promotion is a separate step. That machinery is identical across all thirty-one families and is not repeated per file.

In this section

• Random Forest Classifier
• Random Forest Regressor
• Bagging Classifier
• AdaBoost Classifier
• K-Nearest-Neighbours Classifier
• K-Nearest-Neighbours Regressor
• Linear Support Vector Classifier
• Kernel Support Vector Classifier
• Kernel Ridge Regressor
• K-Means Clusterer
• K-Medoids Clusterer
• K-Modes Clusterer
• Mini-Batch K-Means Clusterer
• Balanced K-Means Clusterer
• Binary Split Clusterer
• Gaussian Mixture Clusterer
• Mean Shift Clusterer
• Isolation Forest Anomaly
• One-Class Support Vector Anomaly
• Mahalanobis Anomaly
• Gaussian Mixture Density
• Principal Component