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.
• ONNX
  The oml.onnx class allows you to import your own ONNX-format model, load it in the database, and score data using the prediction operators of Oracle Machine Learning.
• 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.
• Non-Negative Matrix Factorization
  The oml.nmf class creates a Non-Negative Matrix Factorization (NMF) model for feature extraction.
• Exponential Smoothing Method
  The oml.esm function uses the Exponential Smoothing Method (ESM) algorithm to create a time series model.
• XGBoost
  The oml.xgb class supports the in-database scalable gradient tree boosting algorithm for both classification, regression specifications, ranking models, and survival models. It makes available the open source gradient boosting framework. It prepares the categorical encoding and missing value replacement from the OML infrastructure, calls the in-database XGBoost, builds and persists a model as a first-class database model object, and supports using the model for prediction.