RapidMiner provides learning schemes, models and algorithms and can be extended using R and Python scripts. Individual functions can be called from the command line. Alternatively, the engine can be called from other programs or used as an API. Each operator performs a single task within the process, and the output of each operator forms the input of the next one. Those workflows are called “Processes” in RapidMiner and they consist of multiple “Operators”. RapidMiner provides a GUI to design and execute analytical workflows.
RapidMiner is written in the Java programming language. RapidMiner provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment. RapidMiner uses a client/server model with the server offered either on-premises or in public or private cloud infrastructures.Īccording to Bloor Research, RapidMiner provides 99% of an advanced analytical solution through template-based frameworks that speed delivery and reduce errors by nearly eliminating the need to write code. In 2013, the company rebranded from Rapid-I to RapidMiner.
RAPIDMINER STUDIO AUTO MODEL SOFTWARE
In 2007, the name of the software was changed from YALE to RapidMiner. Starting in 2006, its development was driven by Rapid-I, a company founded by Ingo Merissa and Ralf Klinkenberg in the same year. The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.RapidMiner, formerly known as YALE (Yet Another Learning Environment), was developed starting in 2001 by Ralf Klingenberg, Ingo Merissa, and Simon Fischer at the Artificial Intelligence Unit of the Technical University of Dortmund. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The models were trained, tested, and then evaluated. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).
RAPIDMINER STUDIO AUTO MODEL SERIES
To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival.