I am trying to make predictions (using Weka) on a tabular dataset. It is a categorical dataset
which is encoded by label encoder
.
I got a good result for SVM and Logistic Regression, namely the accuracy is around 85%.
The dataset is high-dimensional and I like to fine-tune my accuracy.
So, I am thinking about the feature selection method. I found different feature selection techniques, such as CfsSubsetEval
, Classifier Attribute eval
, classifier subset eval
, Cv attribute eval
, Gain ratio attribute eval
, Info gain attribute eval
, OneRattribute eval
, principal component
, relief f attribute eval
, Symmetric uncertainty
, Wrapper subset eval
.
I would like to know which one would be the best for the dataset that shows good accuracy with Logistic Regression or SVM?