**🥳 RAPIDMINER 9.9 IS OUT!!! 🥳 **

### The updates in 9.9 power advanced use cases and offer productivity enhancements for users who prefer to code.

## CLICK HERE TO DOWNLOAD

# Estimate values excel

Hi guys, I was doing a job but I found a problem and I don't know how to start, I'm really new to using the rapidminer, and I would like to know if anyone could help me. I have to estimate Feature 8 which is the number of maintenance interventions the device has had. What can I do? Thanks André

Tagged:

0

## Answers

606Unicorn14Contributor I1,619UnicornLindon Ventures

Data Science Consulting from Certified RapidMiner Experts

305RM Data ScientistI worked on your training data a bit to build regression trees based on clean features. The predictive model performs pretty good with 10-fold cross validation. RMSE is as follows

My process attached for your reference.

Cheers,

YY

14Contributor I14Contributor IThanks

André

305RM Data ScientistI used the csv files from you in another thread. They are attached here as well.

Cheers,

YY

14Contributor IThis way I can understand what?

305RM Data ScientistPs. the feat1 could potentially result in some data leakage if we apply target encoding on such categorical attributes with soo many values. I don't have the context here but you can try to drop it by configuring "Target Encoding".

Pps. you can round up the predictions after scoring if you prefer to integers.

HTH!

14Contributor IAndré

305RM Data Scientist14Contributor II hope it makes sense

André

305RM Data ScientistAccording to your definition, the model is predicting " Feat 8, which is the number of maintenance interventions."

I will stick to the

regression models(KNN, regression tree, Random Forest, GLM, GBT are good choices for regression) because you will predict a numerical target. If the target is categorical, saying true/false, broken/normal, then go classification.Besides visualization for data exploration and outlier detection, you can also use some of the outlier detection models (e.g. Tukey test for exponential distribution... )