About Machine Learning Classes and Algorithms

Algorithm Classes

Class	Algorithm	Function of Algorithm	Description
`ore.odmAI`	Minimum Description Length	Attribute importance for classification or regression	Ranks attributes according to their importance in predicting a target.
`ore.odmAssocRules`	Apriori	Association rules	Performs market basket analysis by identifying co-occurring items (frequent itemsets) within a set.
`ore.odmDT`	Decision Tree	Classification	Extracts predictive information in the form of human-understandable rules. The rules are if-then-else expressions; they explain the decisions that lead to the prediction.
`ore.odmEM`	Expectation Maximization	Clustering	Performs probabilistic clustering based on a density estimation algorithm.
`ore.odmESA`	Explicit Semantic Analysis	Feature extraction	Extracts text-based features from a corpus of documents. Performs document similarity comparisons.
`ore.odmGLM`	Generalized Linear Model	Classification Regression	Implements logistic regression for classification of binary targets and linear regression for continuous targets.
`ore.odmKM`	k-Means	Clustering	Uses unsupervised learning to group data based on similarity into a predetermined number of clusters.
`ore.odmNB`	Naive Bayes	Classification	Makes predictions by deriving the probability of a prediction from the underlying evidence, as observed in the data.
`ore.odmNN`	Neural Network	Classification Regression	Learns from examples and tunes the weights of the connections among the neurons during the learning process.
`ore.odmRF`	Random Forest	Classification	Provides an ensemble learning technique for classification of data.
`ore.odmSVD`	Singular Value Decomposition	Feature extraction	Performs orthogonal linear transformations that capture the underlying variance of the data by decomposing a rectangular matrix into three matrices.
`ore.odmSVM`	Support Vector Machine	Anomaly detection Classification Regression	Builds a model that is a profile of a class, which, when the model is applied, identifies cases that are somehow different from that profile.
`ore.odmNMF`	Non-Negative Matrix Factorization	Feature extraction	A state of the art feature extraction algorithm used when there are many attributes and the attributes are ambiguous or have weak predictability.
`ore.odmXGB`	XGBoost	Classification Regression	Can be used as a stand-alone predictor or incorporate it into real-world production pipelines for a wide range of problems such as ad click-through rate prediction, hazard risk prediction, web text classification, and so on.

Persisting Models

In-database models created through the OML4R API exist as temporary objects that are dropped when the database connection ends unless you take one of the following actions:

Save a default-named model object in a datastore, as in the following example:

regr2 = ore.odmGLM("regression")
ore.save(regr2, name = 'regression2', overwrite=TRUE)

Use the model_name parameter when building the model to explicitly name in-database model proxy object, as in the following example:

ore.drop(model='RF_CLASSIFICATION_MODEL')
settings = list(RFOR_MTRY = 3, model_name="RF_CLASSIFICATION_MODEL")
MOD2 <- ore.odmRF(AFFINITY_CARD~., DEMO_DF.train, odm.settings= settings)
MOD2$name

Change the name of an existing model using the model_name function of the model, as in the following example:
```
regr2(model_name = 'myRegression2')
```

To drop a persistent named model, use the oml.drop function.

Scoring New Data with a Model

For most of the OML4R machine learning classes, you can use the predict method of the model object to score new data.

For in-database models, you can use the SQL PREDICTION function on model proxy objects, which scores directly in the database. You can use in-database models directly from SQL if you prepare the data properly. For open source models, you can use Embedded R Execution and enable data-parallel execution for performance and scalability.

Deploying Models Through a REST API

The REST API for Oracle Machine Learning Services provides REST endpoints hosted on an Oracle Autonomous Database instance. These endpoints allow you to store OML models along with their metadata, and to create scoring endpoints for the models.

4.1 About Machine Learning Classes and Algorithms