Scala and Spark for Big Data Analytics

Decision tree versus Naive Bayes

As stated in the preceding table, DTs are very easy to understand and debug because of their flexibility for training datasets. They will work with both classification as well as regression problems.

If you are trying to predict values out of categorical or continuous values, DTs will handle both problems. Consequently, if you just have tabular data, feed it to the DT and it will build the model toward classifying your data without any additional requirement for upfront or manual interventions. In summary, DTs are very simple to implement, train, and interpret. With very little data preparation, DTs can build the model with much less prediction time. As said earlier, they can handle both numeric and categorical ...