The alpha test phase of RapidMiner 5 (internal name: Vega) is about to end and we are looking forward to the upcoming beta test. Today, I would like to describe another great feature of RapidMiner 5, namely the quick fixes. In RapidMiner 5, you will usually retrieve your data from a repository where the data itself together with the meta data is imported and then stored. We will discuss the new repository in one of our next blog entries. One of the main advantages is that we can use the meta data from the repository and let the operators transform it during the process design time.

That means, that the process does not have to be performed in order to get a "picture" of an operator's or even the whole process' outcome. You just have to move your mouse pointer over an output port of an operator and you will get an description of the expected data. This alone is a great feature and has already be mentioned by Simon in one of his posts.

Another nice side effect is that we are now able to better support our users by providing them a collection of hotfixes (we call them "quick fixes") in cases where an operator already detects that it can not be applied on the provided data. Let's think about a simple example: you are going to load the well known Iris data set consisting of numerical attributes only from your repository. You might have decided that you want to model the data with help of the ID3 decision tree learner. Unfortunately, this learning scheme cannot be applied on numerical attributes. In contrast to former RapidMiner versions, this is already detected during process design  time and the user gets a collection of applicable quick fixes, e.g. the user can simply transform the numerical attributes into nominal ones by means of discretization. Double clicking in the quick fix region on the "Problems" tab in the lower part of the screen brings up the quick fix dialog. The quick fix is selected and then applied. That's it: fast and simple.