What's New in Oracle Machine Learning for Python

1 What's New in Oracle Machine Learning for Python

Provides a summary of the latest enhancements and features for Oracle Machine Learning for Python.

Table 1-1 New Features

Features Description

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.

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:

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.