4.2 Choose the Machine Learning Technique

Describes providing an Oracle Machine Learning for SQL machine learning function for the CREATE_MODEL and CREATE_MODEL2 procedure.

An OML4SQL machine learning technique specifies a class of problems that can be modeled and solved. You specify a machine learning with the mining_function argument of the CREATE_MODEL and CREATE_MODEL2 procedure.

OML4SQL machine learning functions implement either supervised or unsupervised learning. Supervised learning uses a set of independent attributes to predict the value of a dependent attribute or target. Unsupervised learning does not distinguish between dependent and independent attributes. Supervised functions are predictive. Unsupervised functions are descriptive.

Note:

In OML4SQL terminology, a function is a general type of problem to be solved by a given approach to machine learning. In SQL language terminology, a function is an operation that returns a result.

In OML4SQL documentation, the term function, or machine learning function refers to an OML4SQL machine learning function; the term SQL function or SQL machine learning function refers to a SQL function for scoring (applying machine learning models).

You can specify any of the values in the following table for the mining_function parameter to the CREATE_MODEL and CREATE_MODEL2 procedure.

Table 4-71 Oracle Machine Learning mining_function Values

mining_function Value	Description
ASSOCIATION	Association is a descriptive machine learning function. An association model identifies relationships and the probability of their occurrence within a data set (association rules). Association models use the Apriori algorithm.
ATTRIBUTE_IMPORTANCE	Attribute importance is a predictive machine learning function. An attribute importance model identifies the relative importance of attributes in predicting a given outcome. Attribute importance models use the Minimum Description Length algorithm and CUR Matrix Decomposition.
CLASSIFICATION	Classification is a predictive machine learning function. A classification model uses historical data to predict a categorical target. Classification models can use Naive Bayes, Neural Network, Decision Tree, logistic regression, Random Forest, Support Vector Machine, Explicit Semantic Analysis, or XGBoost. The default is Naive Bayes. You can also specify the classification machine learning function for anomaly detection for a One-Class SVM model and a Multivariate State Estimation Technique - Sequential Probability Ratio Test model.
CLUSTERING	Clustering is a descriptive machine learning function. A clustering model identifies natural groupings within a data set. Clustering models can use k-Means, O-Cluster, or Expectation Maximization. The default is k-Means.
FEATURE_EXTRACTION	Feature extraction is a descriptive machine learning function. A feature extraction model creates a set of optimized attributes. Feature extraction models can use Non-Negative Matrix Factorization, Singular Value Decomposition (which can also be used for Principal Component Analysis) or Explicit Semantic Analysis. The default is Non-Negative Matrix Factorization.
REGRESSION	Regression is a predictive machine learning function. A regression model uses historical data to predict a numerical target. Regression models can use Support Vector Machine, GLM regression, or XGBoost. The default is Support Vector Machine.
TIME_SERIES	Time series is a predictive machine learning function. A time series model forecasts the future values of a time-ordered series of historical numeric data over a user-specified time window. Time series models use the Exponential Smoothing algorithm. The default is Exponential Smoothing.