Different types of software packages come with various unique tools designed to meet the needs of PC and systems users especially as it relates to a specific purpose, same way this java application called RapidMiner Studio was designed with the aim of providing its users multiple tools to import data and create compound analysis task to help understand and identify data trends.
This program can help users create copies or representation for easy identification of trends identifying linkages between two or more events can be very cumbersome or next to impossible when dealing with huge component so since several businesses depend on obtainable data to make vital decisions, data analyst rely entirely on specialized apps to understand and envisage the information’s.
The RapidMiner Studio benefits to the users focus on providing an effective and efficient intuitive tool that can be used to analyze information from a different origin. It allows users to upload needed information from database servers such as Oracle, MySQL, PostgreSQL or office documents and even plain text folders
When using the RapidMiner Studio, known operators panel allows you to make a selection of needed tools to place it on windows main processor. With this, you can effectively organize the desired tools, link several operators at the same time and indicate the order of execution before kick-starting the analysis
However, just before your analysis ensure to create a procedure and then import the information from the external database or file whereby duplicate data will be detected and also the specification of the data range will ensure errors are duly prevented just because of the included wizard of RapidMiner Studio.
To understand the essential functions of the RapidMiner users can start by using provided templates and sample files because results gotten from the inspection must be shared to other consumers also note the software has a great exporting options to use from.
For inexperienced users and those that are not a data analyst, the list of modeling tools, visualization, the transformation will seem cumbersome and overwhelming but not to worry the software has a sizeable online documentation or archives as well as multiple tutorials needed to access all you need.
RapidMiner Server has transparent integration able to automate procedures for scoring and integration with other applications, data transformation, and model building. It has an open platform APIs and a Marketplace that is extensible with additional functionality. It also has a powerful built-in global search sifts through repositories which can quickly retrieve anything from models, processes, extensions, operators, and even UI actions.
Due to in RapidMiner Studio richness in the data preparation capabilities, it can handle any real-life data transformation challenges that may occur, all you need to do is configure and produce the optimal data sets for predictive analytics. It can blend structured with unstructured information and then leverage for predictive analysis. All information composition process can be saved and stored for reuse.
RapidMiner Studio provides for timely and appropriate estimation of model performance. Where other providers tools appear too closely tied to the modeling and model validation, it follows a rigorous module approach which stops data used in pre-processing tread from escaping from model teaching or instructions into the apps model. This unique perspective guarantees that no oversizing or overestimation introduced for prediction performances may occur.
Key features include the following
- The RapidMiner has totally converted how connections (JDBC, and any other connections like Dropbox, Twitter, Amazon S3, etc.) works
- The links are now self-sufficient and saved per repository. What this means is that when users create a connection, whatever needs to be used will become part of the links entered in the repository.
- It has a high flexibility structure, especially when injecting certain settings of its connection. These settings can be everything from URLs (or part of URLs) or credentials and other parameters. For beginners, the Sources available are only Macro and RM Server nonetheless, the list is structured to grow.
- The application’s interface has a powerful visual program environment
- The RapidMiner Studio is designed to analyze, load and access any type of data and also to be able to extract vital information and statistics
- It is efficiently created to deliver better and faster models, accurately and confidently estimate model performance while scoring models for its platform.
- This software has seamless access and use of algorithms from H2O, Weka and other third-party storage
- The studio has more than 1500 operators for all its tasks transformation and analysis of data and will also support its scripting environments for ultimate extensibility
RapidMiner Studio is an effective and efficient solution provider for the analyst and users who need to imagine, envisage, and understand very complex data. The software comes fully equipped with an exceptional set of modeling capacity and machine learning design for supervised and unsupervised learning. The RapidMiner Studio is very flexible, robust, and allows users to focus on creating the very best possible models for any case.
What's new in 9.5.0 version?
- Added ability to upgrade RapidMiner Studio independently from Server. You can now connect to and access data and processes on older Server versions (9.0 and above) with any current or future Studio version! Processes and data are stored as-is on Server, which enables effective collaboration with your colleagues. However, you need to be aware that while you are able to store processes with brand-new operators on older Servers, you obviously can only run processes that consist of operators that the old Server knows about.
- Deployments: Deployments can now be copied from one to another deployment location (for example from a test to a production server)
- Improved performance of Principal Component Analysis and Weight by PCA operators
- The Import Data dialog now detects files with non-lowercase file extensions
- Fixed view order for Deployments view
- Visualizations: Fixed various issues that could cause Studio startup to fail
- Visualizations: Fixed various issues that could cause them to not be displayed properly
- Auto Model: Decision Tree and Random Forest are now using the latest (faster) implementations for regression problems
- Auto Model: Increased the number of rows for which local explanations are turned on by default
- Auto Model: Loading results from a folder are now adding them to the result list as well
- Auto Model: Shows the total number of feature sets and generated features on the overview as well if automatic feature engineering has been turned on
- Auto Model: The performance tab now shows the gain calculations based on the confusion matrix instead of the predicted data set
- Auto Model: New deploy button in the overview table for each model
- Auto Model: Clicking a model in the overview table will show the details for the selected model
- Auto Model: Prevent another deployment while another deployment is currently performed
- Turbo Prep: Nominal column handling is now consistent to the default behavior of Auto Model
- Turbo Prep: Sort first join keys alphabetically instead of by ID-ness
- Google Storage connection is now replaced by the more general Google Cloud Services connection that can connect to all supported Google services (Google Cloud Storage, Google BigQuery [requires In-Database Processing extension]). Just select the access scopes you want to use
- Fixed all predictions being 0 in Transformed Regression when using no transformation and no z scale
- Fixed the Import Data dialog failing when trying to read an XLSX file which did not have a lowercase file ending
- Repository location chooser now opens as expected if the process is stored in a read-only repository
- Visualizations: Exporting as PDF now also works without internet access
- Visualizations: Fixed broken names in Czech Republic map
- Time Series: Added error handling if the indices attribute was also selected as a time series attribute, or as the horizon attribute
- Deployments: Fixed a bug which broke a local installation of Model Ops after the user connected to an existing remote location with email connection
- Model simulator now works with date / time columns again
- Improved exception handling when Belt tables cannot be converted to example sets
Last Updated: 2019-11-21
Developer: RapidMiner GmbH
File size: 261.25 MB
Operating system: Windows 10, Windows 8/8.1, Windows 7, Windows Vista, Windows XP
MD5 Checksum: 5aae504776995adfff1da677fdcc46df