4 key advantages of using decision trees for predictive analytics
We have demonstrated a couple of applications of using decision trees with open source analytics packages such as RapidMiner. There are several distinct advantages of using decision trees in many classification and prediction applications. Some vendors prefer names such as Classification and Regression trees (CART or C&RT), but they still refer to the same analytics technique at the core. Keep the following factors in mind while considering them for your business analytics applications.
Advantage 1: Decision trees implicitly perform variable screening or feature selection
We described here why feature selection is important in analytics. We also introduced a few common techniques for performing feature selection or variable screening. When we fit a decision tree to a training dataset, the top few nodes on which the tree is split are essentially the most important variables within the dataset and feature selection is completed automatically!
Advantage 2: Decision trees require relatively little effort from users for data preparation
To overcome scale differences between parameters - for example if we have a dataset which measures revenue in millions and loan age in years, say; this will require some form of normalization or scaling before we can fit a regression model and interpret the coefficients. Such variable transformations are not required with decision trees because the tree structure will remain the same with or without the transformation.
Another feature which saves data prep time: missing values will not prevent splitting the data for building trees. This article describes how decision trees are built.
Decision trees are also not sensitive to outliers since the splitting happens based on proportion of samples within the split ranges and not on absolute values.
Get the complete set of articles on decision trees in one place. Download FREE ebook ...
Advantage 3: Nonlinear relationships between parameters do not affect tree performance
As we described here, highly nonlinear relationships between variables will result in failing checks for simple regression models and thus make such models invalid. However, decision trees do not require any assumptions of linearity in the data. Thus, we can use them in scenarios where we know the parameters are nonlinearly related.
Advantage 4: The best feature of using trees for analytics - easy to interpret and explain to executives!
Decision trees are very intuitive and easy to explain. Just build one and see for yourself!
These advantages need to be tempered with one key disadvantage of decision trees: without proper pruning or limiting tree growth, they tend to overfit the training data, making them somewhat poor predictors.
A fundamental question for every business analytics situation is to identify if a given technique is a good fit for the business problem at hand. How can you get better informed, without getting lost in the technical jargon? Sign up for visTASC our FREE analytics education portal.