1. User's Guide
  2. Changes in This Release for Oracle Machine Learning for Python

1 Changes in This Release for Oracle Machine Learning for Python

Describes changes in Oracle Machine Learning for Python User's Guide for Oracle Database 23ai.

New Features in 23ai

Table 1-1 New Features

Features Description

Support for in-database machine learning Non-Negative Matrix Factorization algorithm, Exponential Smoothing Method algorithm and XGBoost algorithm.
The following functions are new in the package that use in-database algorithms:
  • oml.nmf, Non-Negative Matrix Factorization model
  • oml.esm, Exponential Smoothing Method model
  • oml.xgb, XGboost model
Support for data types that enable you to manipulate date, time and integer.

The following data types are supported by OML4Py:

  • oml.Datetime, to create date.
  • oml.Timezone, to create time and timezone that includes hour, minute, second, microsecond, and tzone.
  • oml.Timedelta, to perform simple arithmetic operations.
  • oml.Integer to represent the integer data type.

Convert Pretrained Models to ONNX Format

Oracle Machine Learning for Python supports ONNX format models. To learn more, see Convert Pretrained Models to ONNX Format.

Topic:

The following topic tells about new features added in 23ai.