8 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 anoml
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
Theoml.ai
class computes the relative attribute importance, which ranks attributes according to their significance in predicting a classification or regression target. - Association Rules
Theoml.ar
class implements the Apriori algorithm to find frequent itemsets and association rules, all as part of an association model object. - Decision Tree
Theoml.dt
class uses the Decision Tree algorithm for classification. - Expectation Maximization
Theoml.em
class uses the Expectation Maximization (EM) algorithm to create a clustering model. - Explicit Semantic Analysis
Theoml.esa
class extracts text-based features from a corpus of documents and performs document similarity comparisons. - Generalized Linear Model
Theoml.glm
class builds a Generalized Linear Model (GLM) model. - k-Means
Theoml.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
Theoml.nb
class creates a Naive Bayes (NB) model for classification. - Neural Network
Theoml.nn
class creates a Neural Network (NN) model for classification and regression. - Random Forest
Theoml.rf
class creates a Random Forest (RF) model that provides an ensemble learning technique for classification. - Singular Value Decomposition
Use theoml.svd
class to build a model for feature extraction. - Support Vector Machine
Theoml.svm
class creates a Support Vector Machine (SVM) model for classification, regression, or anomaly detection.