I have two datasets i.e. One is Benchmark dataset and second is Independent dataset. I tested Benchmark dataset using 10-fold cross validation in weka. Can we test Independent dataset in weka?
If you are using Weka GUI:
In Weka explorer - first load your benchmark dataset in the Preprocess tab (use the open file... button). Then go to the Classify tab and there under the test options select Supplied test set press Set... and then open file... and close.
Finally, select the algorithm you want and press Start.
Notice that the train and test files must have the same structure.
Related
How to save the new data set to use it in other applications after performing feature selection in WEKA?
Simply export it in a format such as CSV, JSON, or .arff. In the Weka Explorer, there is a button labeled "Save..." in the top right of the GUI.
Answers to this question explain how to output classification predictions to CSV in Weka in both Weka 3.6 (right / option-click model and then save predictions) and 3.7 (choose more options and select Output predictions).
In Weka 3.7, I chose more options, selected Output predictions, and chose CSV as the specific type of output. An answer suggests to "Click on 'outputFile' and select a folder and type a filename." However, I cannot see 'outputFile' or where the CSV output is saved.
Where is the output file saved, or how can I click on 'outputFile' to name the output?
In Weka 3.7.12 on OSX, I was able to find 'outputFile' and the other options by clicking on the whitebox containing CSV (after choosing CSV first), much like how you specify the options for certain classifiers by clicking on those whiteboxes. For me I wasn't able to type a filename, but if I created a blank file manually, I was able to correctly save the output the predictions to that file.
If I left click on CSV (once I've selected it first), WEKA allows me to select an existing CSV file to save the predictions
I am performing a PCA operation on my dataset using WEKA (filter-unsupervised-principal component). Once I apply, I am getting the PCA. However I am not able to export the PCA in a separate file for further processing. How do I export first 3 PCA in a csv or a txt file from Weka?
The "Save..." button at the top right of the Preprocess tab in Weka Explorer will export your PCA-filtered data. You will be prompted for the name and type of file you'd like to export to.
You can control the number of allowed principal components via the -M parameter to the filter, or you could export to a .csv file, open in a spreadsheet application, and remove all but the first three columns.
I am working on logistic regression classifier of Weka API in java. I want to know How can I set Logistic regression output to instances distributions (the probability to which an instance belongs to class 1) instead of the instances predicted classes? I want to use these to build an ordered list of instances and take top 10 instances.
Any help would be greatly appreciated
I'm guessing here: what you intend to do is similar to this:
Click Preprocess Menu, Click Filter Button, Select "supervised attribute filter", select AddClassification.
Double-Click on "AddClassification"
Select "Logistic", in the config-dialog: select "Output Distribution", select "true". This will add a new column to your dataset, containing the probabilities for each instance.
If this new column will contain any meaningful data, is another matter. You didn't provide enough information, so I took the built-in "credit-g" dataset
Note that there are new columns added below the highlighted "class" column:
The data in these columns look like this:
In SAS Data Integration Studio, under the 'Action' menu, there is an option to 'Propagate Columns'. What is the use of this?
The reads the metadata on the input table and pushes the column definitions into the down stream nodes.
To 'propagate columns' means to move columns from source to target or from target to source in the mapping. You can use action -> propogate only when you select a transformation in the job. then you can propagate columns from source to target for that sas di transformation.