9 OML4Py Classes That Provide Access to In-Database Machine Learning Algorithms

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.
  • 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.