Highest Rated Posts

yyhuang

April 17

Re: Precision of Optimize Parameters Operator?

Hi @Jugi, You can change the settings for display of numbers. It is not precision issues IMO, it is just a preference setup for display all the fractional numbers. Go to menu Settings >> Preferences ... After increasing the number of digits to show, you can get a refreshed list here Best, YY

Telcontar120

April 3

Re: Concatenate Two Numbers

IF you are ok having the resulting field be a numerical instead of nominal, you don't need to concatenate at all.  Just use Generate Attributes to multiply the year by 100, and then add the month number.  So you get 201901, 201902, ...201912.  Like @yyhuang's format, only as a numerical.  This is the way I usually prefer to handle this type of year and month combination.

kypexin

April 1

Replace medium rare values

Hi there,  I am applying REPLACE MEDIUM RARE VALUES operator, and the default doneness it offers is medium well; however, this is not always the desired setting. Is there a way to explicitly set default doneness to rare or medium?  Second question, what is the best practice of applying salt and pepper - before or after replacement?

SGolbert

March 8

Re: Combining RapidMiner with Python and R

Hi @mlg1988 in my opinion R and Python help cover some niche cases which RapidMiner not yet covers, or does it poorly (e.g.: the web mining extension). If you work with the RapidMiner platform, you don't really need to program much, only a few lines to get some special functionality, via the scripting operators. The project framework that RM gives you is unparalleled in the R and Python world. There are some cases when some tasks are easier to do with a script than in RapidMiner, in those cases I would insist with RM for a given time, because once you solve a problem in RM, the future use of the process (changes, debugging, operationalization) is better if you don't depend on external tools. Regards, Sebastian

sgenzer

March 4

Cross-Validation and its outputs in RM Studio

We get a lot of questions about how exactly the Cross Validation operator works, and what those 'exa', 'mod', and 'tes' outputs produce, in RM Studio. Hence it seems a good idea to explain this in depth as a KB article. So here I go... What is Cross Validation? Cross Validation is a method used to find a true estimate of the performance of mathematical models built on relatively small data sets. It builds a series of models on subsets of the data and tests each model on the remainder of the data to determine an average performance metric of the models, rather than one performance metric of one model. Split Validation, a much simpler process where you just split the data once into a 'training' and 'testing' set, works very well for very large data sets. But it's Achilles heel is its assumption that the testing set is a representative sample of the entire data set. This is likely true for large data sets, but less and less likely the smaller your data set is.  How does Cross Validation work? Cross Validation works by dividing the data set first into n partitions, and then building n unique models one at a time (each iteration is called a "fold"). For each iteration, a model is created using n-1 partitions and using the nth partition. The model itself is not relevant; what is retained is the model performance. This is repeated using a different partition as the hold-out set until each partition is used. After the n performances are calculated, we calculate the average performance (and its standard deviation) of all n models built during the cross validation. Lastly, we build a true model with the entire data set and display this calculated average performance. Very Simplistic Example of Cross Validation Let's walk through exactly how it works using a very simple example. Here is a data set where we have one independent variable (x) and one dependent variable (y). The goal is to produce the best linear regression model of these data using cross validation. The y values were randomly generated. In this example we're going to use linear sampling, 5-fold cross validation, and RSME as our performance metric: * Linear sampling - we will pull our hold-out sets in order which the data sets is given to us * 5-fold Cross Validation - we will perform five iterations to find five performances using five hold-out sets * RSME - the root-mean-squared-error of the predicted values of each hold-out set vs the actual values Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Hence: average RSME =  avg (0.347, 0.286, 0.306, 0.289, 0.321) = 0.310  standard deviation of RSME = stdev (0.347, 0.286, 0.306, 0.289, 0.321) = 0.025 The model representing this performance metric is none of the models shown above; it is a new model produced on the entire data set. Sometimes this is considered the "n+1" th iteration. Calculate the actual model What are the outputs of the Cross Validation operator in RapidMiner Studio? Ok now we can answer this question! They are as follows: * mod - the actual mathematical model built on the entire data set (see last step above). Usually only the relevant coefficients or equivalent are displayed. * exa - the original ExampleSet (data). This is simple a pass-thru. * tes - the testing ExampleSet. Note that this is NOT the application of the final model on the data set; rather it displays all the hold-out testing sets from the n-fold cross validation in one appended list. * per = the average performance of the n-fold cross validation For our example, these are as follows: Why should we go through all this trouble? I am going to just insert a few paragraphs from founder @IngoRM here: (Quote) Hope this helps some people. Thank you @yyhuang and @IngoRM for your help!  Scott

kypexin

March 1

Re: how to select important variables for my model

Hi @ed1305 New AUTOMATIC FEATURE ENGINEERING operator does a great job here, that's the one I use every time I need to pick the best features out of original dataset. 

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