weka … You need to import train_test_split and NumPy before you can use them, so you can start with the import statements: >>>. 5. Generate the tree visualizer. from a set of classiers provided by WEKA (Hall et al., 2009) by measuring the performance of several classiers on testing with the training dataset, 10-fold cross-validation, and by percentage split which divides the training set into 60% for training and 40% for testing. A common split value is 66% to 34% for train and test sets respectively. In the percentage split, you will split the data between training and testing using the set split percentage. I am using weka tool to train and test a model that can perform classification. The rest of the data is used during the testing phase to … javaaddpath('weka.jar'); import weka.core.Instances. weka.filters.unsupervised.instance.RemovePercentage java code A dialog window appears showing various types of classifier. Open the weka explorer. The first step is to download the Weka GUI. An 80% percentage split will train a model on 80% of our data. Help understanding and implementing percentage split for evaluation using WEKA API; Results 1 to 2 of 2 Thread: Help understanding and implementing percentage split for evaluation using WEKA API. classification - J48 decision trees in weka - Cross Validated