Does test file in weka requires same or less number of features as train?

Question

I have prepared two different .arff files from two different datasets one for testing and other for training. Each of them have equal instances but different features chang

Answer 1

According to Weka's wiki, the number of features needs to be same for both the training and test sets. Also the type of these features (e.g., nominal, numeric, etc) needs to be the same.

Also, I assume that you didn't apply any Weka filters to either of your datasets. The datasets often become incompatible if you apply filters separately on each dataset (even if it is the same filter).

Answer 2

Does test file in weka requires same or less number of features as train ? Code for evaluation

Same number of features are necessary. You may need to insert ? for class attribute too.

According to Weka Architect Mark Hall

To be compatible, the header information of the two sets of instances needs to be the same - same number of attributes, with the same names in the same order. Furthermore, any nominal attributes must have the same values declared in the same order in both sets of instances. For unknown class values in your test set just set the value of each to missing - i.e "?".

Answer 3

How do I divide a dataset into training and test set?

You can use the RemovePercentage filter (package weka.filters.unsupervised.instance).

In the Explorer just do the following:

training set:

-Load the full dataset

-select the RemovePercentage filter in the preprocess panel

-set the correct percentage for the split

-apply the filter

-save the generated data as a new file

test set:

-Load the full dataset (or just use undo to revert the changes to the dataset)

-select the RemovePercentage filter if not yet selected

-set the invertSelection property to true

-apply the filter

-save the generated data as new file