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.
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. - Non-Negative Matrix Factorization
Theoml.nmf
class creates a Non-Negative Matrix Factorization (NMF) model for feature extraction. - Exponential Smoothing Method
Theoml.esm
function uses the Exponential Smoothing Method (ESM) algorithm to create a time series model. - XGBoost
Theoml.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.