Bewertet am 11.6.2019
The data scientist's swiss knife: Fast and Easy Machine Learning through RapidMiner Studio
Maynard john S.
Kommentare: Overall my experience with using RapidMiner was great. It allowed me to rapidly try out different machine learning models and compare each result with one another. It also allowed me to conveniently address my workflow without having to write code. It is a great tool for students and people without a strong programming background. Its well documented functions and strong community addresses what ever questions I had with the processes.
One of the daunting requirements for data scientists and data storytellers is learning a programming language such as matlab and python and writing code for their tasks. This is on top of having to analyze and learn complex algorithms needed for the task. This can be a time consuming problem, especially for those who are not adept at programming. However, this is now a thing of the past because of RapidMiner Studio.
This is because RapidMiner features are drag and drop visual interface which makes all the difference. Data preparation to the final output and visualization is as simple as dragging blocks of your workflow into a canvas and connecting them altogether.
RapidMiner Studio also has most of the machine learning models used in the academe and the industry. One of the difficulties when dealing with code is tweaking the parameters of these models but because of the visual interface, you could simply click on the process and update this. RapidMiner is also well documented. Each of the processes has their description, input, output, and parameters well described. Tutorial videos as well as blogs are available on their website. And finally, RapidMiner Studio has a community of data scientists that can help you when you have a question.
Nachteile: What I found to be very inconvenient is that the application crashes at times. This may be a problem limited to my own machine. Aside from this I found that the application seems to hog my computers memory and cpu resources. This may be because the application is running on Java (VM). This may not be a problem for people with a higher spec machine. I also found that the application lacks collaboration features which may be something that they could improve on in the future.
Bewertet am 1.8.2018
Great tool for data analytics!
Kommentare: Great data analytic and visualization tool!
Vorteile: No coding skills needed! Rapidminer is a GUI tool that you can connect boxes on a canvas to conduct data anlysis, this serves as a great introduction to data analytics. Free for students! You can get a provisional liscence with a dot edu account. This is a great perk of the software. Data analytics and data visualization tools are available within the software with a plethora of other features!
Nachteile: Very buggy! The software tends to crash often, this is especially more common with things such as neural networks etc. Limitations of some versions! Even with the student version there is a limit of 10,000 rows of output, so if you are trying to do analysis on a 12,000 point data set , 2000 points will randomly be omitted.
Bewertet am 20.11.2018
Excellent tool for Data Mining
Vorteile: Started using this software a few months ago, and its shear power is amazing. The number of things the user can do with this software are amazing
Nachteile: Sometimes, while handling big data, like having large number of examples and attributes, it takes a lot of time. The cumulative time increase, when the user is optimizing manually different attributes based on the results
Bewertet am 30.10.2018
Easily do your data mining
Vorteile: I learnt how to do data mining with this software. I can only say it is perfect even for new learners. When I started data mining the concept itself was new to me and I did not want to do complicated programming to do my data mining. That is why I chose RapidMiner. It is so easy to use and it provides lots of hint how to use each icon. I really prefer RapidMiner if I want to get my data mined faster and easier. Highly recommended.
Nachteile: The help section was not %100 complete. It has many explanation but examples are not good explained.
Bewertet am 10.5.2018
Its simplicity enables one to do data science and the best part is you can customize as per need.
Vorteile: Very comfortable to use especially for a beginner and since it can be supported in any platform and excellent form of teaching.Makes machine learning easy
Nachteile: A bit pricey to acquire but worth it at the end and it can consume a lot RAM sometimes your computer freezes
Bewertet am 1.11.2018
Toward sophisticated data analytics :RapidMiner
Kommentare: I use RapidMiner for data analytics, data mining, classification and clustering in construction management.
Vorteile: 1- Friendly interface, robust software operation 2- Easy to learn 3- It support several data types
Nachteile: Graphic plotting capabilities compare to R is low, I think this important feature that make RapidMiner stronger.
Bewertet am 16.11.2017
Fairly straight forward analytics
Kommentare: I'm using the basic version but was surprised to be able to easily analyse my small data set and identify a few interesting trends very swiftly. Usually I'd do basic analysis manually with SQL and gnuplot, but can see myself using to Rapidminer more in future (not something I thought I'd say)
Vorteile: Provides a surprising array of different analytical templates from to off. Very easy to get up and running, an online tutorial videos quickly show how to use the different analytical reports.
Nachteile: I always prefer free versions of software to be open source, but that's perhaps being too picky. Learning curve.
Bewertet am 1.2.2017
Great tool for Proof of Concept purposes
Kommentare: I used RapidMiner a lot for doing proof of concept of some machine learning models before going to the production. It is really easy to construct a machine learning workflow, including loading data, features selection and cleaning, applying machine learning models and visualization. Sometimes, RapidMiner does not work well with big data as it requires a lot of memory to process the data. However, for me, it is the best tool yet to do pre-production experiements.
Easy to use.
Perfect for non-technical users.
RapidMiner includes a lot of Machine Learning libraries and algorithms.
Easy to construct simple and understandable machine learning workflows
Does not scale well with the big data.
Some visualization techniques are ambiguous.
Bewertet am 17.3.2019
Userfriendly Interface for ETL
Kommentare: It's a good platform while applying predictive analytics on any dataset with a user friendly interface to have the true picture of the future.
Visual workflow designer is the best part for predictive analytics.
I really like drag n drop interface for generating models.
Prebuilt templates are quite useful.
It's not always free. You need to purchase it if you wana work with more than 10000 rows.
Its processing becomes too slow (almost hangs) while working with terabyte or petabyte of data.
Sometimes, it becomes difficult to handle hundreds of models available.
Bewertet am 7.11.2018
do you need to analyze the data?
Vorteile: I had a big data set I should analyze and didn't have any clue about data mining that's where I was introduced with rapid miner and I analyzed my data in less than a day. so I can just say its so easy and pretty simple and perfect.
Nachteile: I couldn't find any instructions and manual as a guideline for using it.
Bewertet am 30.4.2019
Best bang for your buck analytics tool, but only for Windows
Vorteile: This tool is basically identical to Alteryx, but substantially cheaper - making it a better value pick
Nachteile: This tool only worked well for my teammates who are in Windows....anyone who was using Mac had consistent issues (even after reinstalls), making it hard to pick for a team that uses both Windows and Mac
Bewertet am 6.12.2016
Very fast work and excellent presentation of results
Kommentare: I combined RapidMiner with R and it is a wonderful tool. It is the best in the market to build useful information from the result of data ming process.
Vorteile: Easy of use, fast, really nice presentation of results.
Nachteile: The expansion through code is not easy. It has a lot of functionalities but in some locations you got stuck and need to implement in other way.
Bewertet am 30.7.2019
Great tool to enter with Data Science Analysis and Machine Learning
Kommentare: I used Rapid Miner in a certification of Big Data and Machine learning. It was tremendous support on the first stage of this courses
Graphic tool with lot of Predictive tools .
Support Python code
Support easy and complex of data sources
Great tool for beginners and experts
Needs a good computer to support the engine.
Output Graphics are not the best and I used excel to improve my presentations
Bewertet am 7.3.2018
Rapid Miner is a fantastic tool for machine learning!
Kommentare: It allowed us to do predictions on our data without having to be an experienced Data Scientist
Vorteile: It's so intuitive and easy to use. You can have a machine learning model built in just minutes and the price is great too! The best thing is that I don't have to be an experienced Data Scientist to work with this too. It's a great tool at a great price and very easy to use compared to other tools on the market!
Bewertet am 18.12.2018
RapidMiner Data Modelling
Easy to transform data and model
easy to use machine learning algorithms
Options for feature customization
webservice is difficult to use
the GUI is not aesthetic
Bewertet am 27.7.2019
Good but demanding
Kommentare: I used Rapid Miner as part of a data mining lab at the university. It was pretty amazing what can be done with just a few clicks.
Vorteile: It makes data mining extremely easy even for beginners. Has many ready to go mining methods and drag and drop design.
Nachteile: It is very resource hungry. Makes memory full after a while. Despite its ease of use, it is also easy to get lost with so many features.