- #Rapidminer studio prediction based on weights mod
- #Rapidminer studio prediction based on weights download
- #Rapidminer studio prediction based on weights free
- #Rapidminer studio prediction based on weights windows
Over-fitting implies that the model even tries to fit to the noise that appears in y=1 record cases. The model, therefore, over-trains (or over-fits) itself when y=1. The number of (cross-validated) training cases with y=2 is much less (=1490) than the number of training cases with y=1 classification (=10873). The answer to second question can be seen in the number of cross-validated training cases with y=2. Prediction for y=1 has more accuracy than for 2. Which measure of precision to rely on? And why is there so much difference? The answer to first question depends upon which prediction is more important for you. To be brief, the logistic model is as follows:įigure VIII: Confusion matrix (click image to enlarge)
There is a whole lot of output in the Results window. On my i7, 8GB laptop, it took 40 minutes. Model building and testing is quite time consuming. Similarly ave port of split-validation operator is connected to Results window. Apply Model‘s two output ports, one with labelled (classified) data (lab port) and the other the model itself (mod port) are connected to inputs of Results window. Another input port of Apply model receives data from tovalidate data-source.
#Rapidminer studio prediction based on weights mod
The output of mod port is fed into Apply model‘s input mod port. The output of split-validation operator contains two outputs: the constructed logit model (mod output port) and performance evaluation (ave output port). All this model building and testing happens within Split Validation operator itself. It uses the 70% stream to build a logistic regression model (Figure VI, left panel) and to the remaining 30%, it applies this model to evaluate its performance (Figure VI, right panel). What we have done is this: In the Process window, Split-validation operator splits the incoming data stream (from training.csv) in 70:30 ratio. Complete all port connections as shown in Figure V. From the repository (Figure I), drag imported tovalidate.csv data source and also (from Operators window) drag Apply Model operator into the center Process window. Shift back to Process window (by clicking on Process just above the left panel). Drag Apply Model ( Modeling->Model Application->Apply Model) and Performance (Evaluation->Performance) operators to right panel. Search for Logistic Regression operator and drag it into the left-panel. Drag Logistic regression operator, Apply Model operator and Performance operator to the two parts of window as shown. Import into RapidMiner repository, data file training.csv as shown in the figures below.įigure-VI: Split-validation window. At the same time, we use this model to classify for us hitherto unclassified data (data in tovalidate.csv file). (Operators: Split-validation Performance Apply model)ĭ. 70% of records go into building model, and 30% records are used to gauge model’s performance. Training data will be split in 70:30 ratio. Given training data (training.csv), we will build a logistic regression model. Import files training.csv and tovalidate.csvī. Using the bash script as mentioned in Part-I, the file bank-full.csv was ripped into two files around 4000 randomly selected records were stored in tovalidate.csv file and training.csv file was left with the remaining around 41000 records.Ī. In what follows, some little familiarity with RapidMiner operators will be desirable. bat extension as appropriate for your OS double-click it to start RapidMiner. Inside scripts folder, look for file: RapidMinerGUI.
#Rapidminer studio prediction based on weights download
Declare JAVA_HOME, download and unzip the package and it is ready for work.
#Rapidminer studio prediction based on weights windows
Being Java based, RapidMiner can be run in either Windows or in Linux.
#Rapidminer studio prediction based on weights free
The free versions limitation is that complete data should be in memory for analysis. The Starter and (open-source) community versions of RapidMiner (rapidminer 5.3.015) are free. RapidMiner Studio 6 can be downloaded from here. In this blog, we proceed first with setting up RapidMiner Studio for conducting the experiment and then discuss results.
In Part-I of the blog, we have described the data set for logistic modelling.
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