Scripting panel, click RscriptExecutor onto canvas.

double-click ArffLoader to load a data file.

Data mining processes panel, click DataSources to choose ArffLoader for example, click it onto canvas.

go to R console panel, type R scripts inside R console box.

preprocess panel, click open file, choose a data file from weka data folder.

Go to directory of Weka 3-8-0, open its terminal, run the following code: java -jar weka.jar

Next time, you just need to go to the directory's terminal and run. Paste weka.jar from weka 3-8-0 into the directory and run the code below: Make it executable, inside this directory's terminal, run the code below: To make life easier, inside a directory where you want to work with weka, save the code above into a file named as weka_r.sh Run the following 2 lines of codes (thanks to Michael Hall)Įxport R_HOME=/Library/Frameworks/R.framework/Resources Go to weka 3-8-0 folder (if it is the version you are using), and open its terminal, and Install Rplugin with Weka Package Manager Replace the old info.Plist with the new one given by Mark Hall

R in Weka, can easily get data from, process it, and pass to Weka seamlessly.

R has a huge number of libraries and keeps growing.

R is a powerful tool for preprocessing data.

The second row is equivalent for instances classified as no. In the first row, for example, it tells you the number of instances classified in your training data as yes that you classified as yes (that is, 7) and the number that are classified as yes that you classified as no(2). The odds for outlook=overcast are extremely favorable to the yes outcome, producing a high positive value.Ī simpler example of this can be found here.ģ.- The confusion matrix is very simple. Now you can see why outlook=overcast has such a value. However, in this particular example, since you have more than one variable as predictors, it's necessary to fix the value of the other variables. The log of this ratio is the value of the coefficient attached to the variable outlook=sunny in the logistic regression. Similarly, you can calculate the odds for outlook=¬sunny.

I use numeric data from WEKA examples: outlook $$įor instance, the odds of outlook=sunny is the probability of a sunny day in which you can play over the probability of having a sunny day in which you can't play. Please help interpret results of logistic regression produced by from the WEKA library.

Weka jar online