Oracle Machine Learning Algorithms
Oracle Machine Learning provides a wide range of machine learning algorithms to address key enterprise business problems. "Move the algorithms, not the data!" is a tagline that characterizes algorithms that exist in the Oracle Database kernel or are available on Big Data environments. These algorithms are accessible through several convenient interfaces to create and deploy machine learning solutions.
Key
- Native Oracle implementation + - Embedded R execution implementation 3p - Integrated third-party implementation Note: documentation links are for Oracle Database algorithms| Technique | Algorithm | OML4SQL | OML4R | OML4Py | ODMr | OML4Spark |
| Classification | Decision Tree |
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3p |
| Logistic Regression (GLM) |
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| Naive Bayes |
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| Neural Network |
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Roadmap |
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| Random Forest |
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| Support Vector Machine |
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3p | |
| Gradient Boosted Trees (XGBoost) | Database 21c |
Roadmap | Roadmap | 3p | ||
| Explicit Semantic Analysis (ESA) |
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Roadmap |
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| Regression | Generalized Linear Model (GLM) |
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| Linear Regression |
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3p |
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| Neural Network |
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| Ridge Regression |
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| Support Vector Machine |
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| Gradient Boosted Trees | Database 21c |
Roadmap | Roadmap | 3p | ||
| Random Forest | Database 21c |
Roadmap | Roadmap | 3p | ||
| LASSO | 3p | |||||
| Decision Tree | 3p | |||||
| Clustering | Expectation Maximization (EM) |
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| Gaussian Mixture Model (GMM) |
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3p | |
| Hierarchical K-Means |
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3p |
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| Orthogonal Partitioning (O-Cluster) |
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| Feature Extraction | Explicit Semantic Analysis (ESA) |
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| Non-Negative Matrix Factorization (NMF) |
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Roadmap |
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| Principal Component Analysis (PCA) |
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3p |
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| Singular Value Decomposition (SVD) |
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| Low Rank Matrix Factorization |
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| Association Rules | A Priori |
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| Anomaly Detection | One Class Support Vector Machine |
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| MSET-SPRT | Database 21c |
Roadmap | Roadmap | |||
| Attribute Importance | Minimum Description Length |
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| CUR Matrix Decomposition |
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Roadmap | Roadmap | |||
| Time Series | Exponential Smoothing |
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Roadmap |