OML4Py has classes that provide access to in-database Oracle Machine Learning algorithms.

These classes are described in the following topics.

• About Machine Learning Classes and Algorithms
  These classes provide access to in-database machine learning algorithms.
• About Model Settings
  You can specify settings that affect the characteristics of a model.
• Shared Settings
  These settings are common to all of the OML4Py machine learning classes.
• Export Oracle Machine Learning for Python Models
  You can export an oml model from Python and then score it in SQL.
• Automatic Data Preparation
  Oracle Machine Learning for Python supports Automatic Data Preparation (ADP) and user-directed general data preparation.
• Model Explainability
  Use the OML4Py Explainability module to identify the important features that impact a trained model’s predictions.
• Attribute Importance
  The oml.ai class computes the relative attribute importance, which ranks attributes according to their significance in predicting a classification or regression target.
• Association Rules
  The oml.ar class implements the Apriori algorithm to find frequent itemsets and association rules, all as part of an association model object.
• Decision Tree
  The oml.dt class uses the Decision Tree algorithm for classification.
• Expectation Maximization
  The oml.em class uses the Expectation Maximization (EM) algorithm to create a clustering model.
• Explicit Semantic Analysis
  The oml.esa class extracts text-based features from a corpus of documents and performs document similarity comparisons.
• Generalized Linear Model
  The oml.glm class builds a Generalized Linear Model (GLM) model.
• k-Means
  The oml.km class uses the k-Means (KM) algorithm, which is a hierarchical, distance-based clustering algorithm that partitions data into a specified number of clusters.
• Naive Bayes
  The oml.nb class creates a Naive Bayes (NB) model for classification.
• Neural Network
  The oml.nn class creates a Neural Network (NN) model for classification and regression.
• Random Forest
  The oml.rf class creates a Random Forest (RF) model that provides an ensemble learning technique for classification.
• Singular Value Decomposition
  Use the oml.svd class to build a model for feature extraction.
• Support Vector Machine
  The oml.svm class creates a Support Vector Machine (SVM) model for classification, regression, or anomaly detection.