InRule Technology, a company specializing in integrated decisioning, machine learning, and process automation software, has announced its addition of machine learning modeling engines to its suite of offerings. This innovation integrates downsampling and model calibration, and it enables teams to create machine learning models faster without sacrificing interpretability.
Downsampling reduces data sets, allowing ML models to be trained more quickly, especially when dealing with a low prediction target. This feature also decreases cloud costs for Model Ops infrastructure. As a complement, InRule Machine Learning users can take advantage of new model calibration functionality before model deployment. This allows users to realign model outputs to the rate of actual occurrence, improving the interpretability and actionability of adjusted data sets.
Danny Shayman, the InRule Machine Learning product manager, shared that Many of our customers most common use cases involve highly class-imbalanced data sets, and this pair of capabilities will significantly reduce their model training times while retaining full interpretability.
These newly added capabilities from InRule Technology can help businesses streamline their automation processes and improve their productivity in the long run. Downsampling and calibration via machine learning modeling engines make big data more accessible for automation, making it easier for businesses to improve their decision-making and outcomes.
Frequently Asked Questions (FAQs) Related to the Above News
What is InRule Technology?
InRule Technology is a company specializing in integrated decisioning, machine learning, and process automation software.
What has InRule Technology recently announced?
InRule Technology has announced the addition of machine learning modeling engines to its suite of offerings.
What features does the addition of machine learning modeling engines include?
The addition of machine learning modeling engines includes downsampling and model calibration.
What is downsampling?
Downsampling is the process of reducing data sets, allowing ML models to be trained more quickly, especially when dealing with a low prediction target.
What is model calibration?
Model calibration is the process of realigning model outputs to the rate of actual occurrence, improving the interpretability and actionability of adjusted data sets.
How can downsampling and calibration benefit businesses?
Downsampling and calibration via machine learning modeling engines make big data more accessible for automation, making it easier for businesses to improve their decision-making and outcomes.
What are some use cases for downsampling and calibration?
Downsampling and calibration are useful for highly class-imbalanced data sets, and can significantly reduce model training times while retaining full interpretability.
Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.
