Highest Rated Posts

lionelderkrikor

January 9

Re: Discretization before or after Feature Selection?

Hi @green_tea, I would say that Discretize the data must be performed before Feature Selection on the training set :  Don't forget to apply the same pre-processing step(s) to your test set... Hope it helps, Regards, Lionel

yyhuang

January 7

Re: Random Forest - Attribute Importance

Hi @asav_yu, have you run the model simulator in Auto Model? If you try "simulator" operator on your random forest trees, it will show you how the input "customer income" would affect the prediction interactively.. https://rapidminer.com/products/auto-model/ &lt;?xml version="1.0" encoding="UTF-8"?&gt; <output> </output>

varunm1

January 5

Re: Function of prediction

Hi @koknayaya, I worked on a similar problem and this is a bit tricky question. This is mainly based on what you are trying to achieve. From your question I see that you are asking the difference between pattern recognition and machine learning.  So, Pattern recognition is a sub category of Machine learning. What machine learning does is that it try to pick useful features and learn from them based on statistical models to predict. This is the major difference. So, Pattern recognition is a problem type whereas Machine learning is works on solution for that. Thanks, Varun

lionelderkrikor

January 3

Re: Is there a way of using RM Studio just to score new examples without the process learning each time?

Hi @mark_stern, Of course it's possible !  1. Use the Store operator to store your trained/validated model (in a repository you have to set) like this : 2. Open a new process and Retrieve the model you saved and score your new data via the Apply Model operator :  I hope it helps, Regards, Lionel

David_A

January 2

Re: Prediction of Time Series Data

Hi, for time series forecasting you can use the ARIMA or Holt-Winters forecasting operators, when you have a uni-variate time series. If you have several features per time stamp, you can also use the Windowing operator to transform your series data into a set of windows, on which you can then apply Auto Model or your own prediction algorithm. Take a look at the example process below, taken from the Samples folder in RapidMiner. Best, David &lt;?xml version="1.0" encoding="UTF-8"?&gt; <output> Type your comment Retrieve the German gas prices data set from the Samples/Time Series folder.&lt;br&gt;&lt;br&gt;Remove the first 16 Examples, so that the remaining Examples starts at 9:00 AM Perform a Windowing on the data set.&lt;br&gt;&lt;br&gt;The window size is set to 48, to include the prices of the previous 48 hours for each window.&lt;br&gt;&lt;br&gt;The step size is set to 24, so that we only look at windows which ends at 8:00 AM.&lt;br&gt;&lt;br&gt;The horizon size is set to 1, cause we want to forecast 1 price.&lt;br&gt;&lt;br&gt;The horizon offset is set to 23, so that the horizon is 23+1 hours after the window, hence the gas price of the next day at the same time.&lt;br&gt;&lt;br&gt;The resulting ExampleSet contains all we need to train any machine learning model on it. A label (the price of the next day, (gas price / euro cents (times 1000) + 24 (horizon); 48 Attributes containing the prices of the last 48 hours (gas price / euro cents (times 1000) - i) and a special attribute holding the last date in window, which is not used in the training). Train a Gradient Boosted Tree on the ExampleSet created by the Windowing operator. </output>

pschlunder

December 2018

Re: Modeling Long- and Short-Term Temporal Patterns

Hi felix_w, look out for tomorrows update. Recurrent layer handling (and for now LSTM layers first) will be added to the extension. Then you'll be able to perform multivariate time-series forecasting with the DL ext. as well without having to rely on Keras. It will also include some sample processes. Btw: Also included will be an operator to load models trained in Keras and apply them in RapidMiner without having to install keras. Regards, Philipp

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