please answer correctly with explanation in Artificial Intelligence

Question:

You are given a data of primarily categorical data. Your goal is to classify the data over 5 labels. You have a large data set with many features. You train your model using sklearn.tree.DecisionTreeClassifier and the default options. You get a F1 score of 0.98 on your training data, but you get a F1 score of 0.62 on your test data.

What type of problem are you encountering? What can you do to get better test results? Your answer can either recommend changes to pre-processing, model parameters, and/or using a different technique.