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Hi, I just have a couple of minutes so I thought I will weigh in a bit on this topic :smileyhappy: This is in fact really important to me. So here we go... I personally think it is important to note … (View Post)

Yip, that's right. By the way, the way how we are doing the performance estimation in AM is actually quite clever. The reason for why cross-validation is a more robust estimator is that it reduces th… (View Post)

Hi! I don't have a readily available sample workflow. This is a complex process. But you're trying to solve a complex problem, so that's expected. The outer loop (for the samples) could be a plain Lo… (View Post)

Hi! The connections framework has been introduced long ago, and the old framework that included Manage Database Drivers is not available anymore for new installations. Nowadays you create connection … (View Post)

Hi! Yes. RapidMiner's Optimize Parameter operators are primarily meant for supervised learning methods (classification and regression). Optimize Parameters expects a "performance vector". U… (View Post)

Hi! There should be an icon for RapidMiner Studio in your start menu or the equivalent on your operating system. If you can't find it there, you can still browse to the installation directory on your… (View Post)

Hi @RM_Newbie, Your connection is ready, so you may want to use it in an operator. If you have the In-Database Process Extension, you should be able to find the In Database Nest operator. Add one and… (View Post)

Hi, this is quite straightforward. You just import or open your data (using Read Excel), set the target attribute to label (using Set Role) and connect this to a Linear Regression operator. The model… (View Post)

Dr. Phasit Charoenkwan Data Scientist CAMT, Chiang Mai University ปกติชอบเขียน PyThon เวลาทำงานมากกว่า แต่ RapidMiner เอาไว้สอนเด็กๆครับ ฮ่าๆ เป็น อ. ธรรมดาๆ ครับ แต่มีโอกาศทำมาเกือบทุกเรื่องตั้งแต่ย… (View Post)

Hi @Deadrock007, Have you try with Filter Examples Operator? You can use it to remove data with your own defined filters. Best, Cesar (View Post)