Automatized pipeline for attribute and model selection
I am new to Rapidminer, but I already tried some models on my data out and it worked perfectly.
I have the following data set:
40 examples and about 100 attributes (numbers: real), 1 label (binominal).
My aim is to find a good model that predicts the label using just a few attributes like eg 4.
I tried using the tutorial “Finding the right Model” (https://www.youtube.com/watch?v=uN1I4yrNNuQ) multiple models (Decision Tree, Naive Bayes, k-NN, Neural Net, Linear Regression…) using “Compare ROCs”. This worked good and inspired me for this question.
I want to set up a pipeline that does the following tasks:
- Randomly, or weight based selects attribute combinations (eg only 4 attributes: 2 attributes that I manually selected and the other two are randomly selected)
- Forwards them to a X-Validation function that uses multiple models on the data (Decision Tree, Naive Bayes, k-NN, Neural Net, Linear Regression...)
At the end I get a report for each tested attribute combination, used model and the performance measurements of the model (eg. accuracy). At best ordered by accuracy.
Is there such a pipeline?
Does anyone know what I have to put together to realize such a pipeline?
Thanks for your help.