Building a simple text classification experiment – Training and evaluating a simple predictive model
I used LightSIDE tool as explained by Dr. Carolyn to run a simple classification experiment. The tool is easy to use and straightforward if we follow the steps.
In the Extract Features pane, I loaded the NewsgroupTopic dataset from the sample data directory in LightSIDE. I selected Unigram and Bigram features and clicked on Extract. I then saved the feature space for later use.
In the Build Models pane, I used the recently created feature table. I selected Naive Bayes as the learning plugin, set the number of folds to be 20 for cross-validation and clicked on Train.
I got Accuracy 58% and Kappa = 0.44 for the model as given in the assignment, which means my steps were correct 🙂