Machine Learning - Enhancements for R
Exponential Smoothing Method (ESM) for Time Series Forecasting
Exponential Smoothing is a moving average method with a single parameter which models an exponentially decreasing effect of past levels on future values. This in-database algorithm is exposed through the R API of Oracle Machine Learning for R.
Exponential Smoothing Methods have been widely used in forecasting for over half a century. It has applications at the strategic, tactical, and operation level. Being exposed as part of the R API, you have native R access to this in-database algorithm.
In-Database Neural Network Algorithm in OML4R
The in-database Neural Network algorithm allows you to address classification and regression use cases.
Neural networks are well-suited to data with noisy and complex patterns, such as found in sensor data, and provide fast scoring. Being exposed as part of the R API, you have native R access to this in-database algorithm.
In-Database Random Forest for Classification in OML4R
The Random Forest algorithm provides an ensemble learning technique for classification.
Random Forest is a popular classification algorithm due to its high predictive accuracy. You can now use this in-database algorithm through the R API of Oracle Machine Learning for R.
XGBoost Support for Classification and Regression in OML4R
XGBoost is a scalable gradient tree boosting algorithm that supports both classification and regression. The in-database implementation makes available the XGBoost Gradient Boosting open source package.
XGBoost is a popular classification and regression algorithm due to its high predictive accuracy and its support for the machine learning technique survival analysis. Being exposed as part of the R API, you have native R access to this in-database algorithm.