{'fraction_non_alphanumeric': 0.021369596891694997, 'fraction_numerical': 0.0009713453132588635, 'mean_word_length': 5.280487804878049, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Decision Trees as a Predictive Modeling Method

Predictive modeling has become an important area of interest for people who in areas such as credit scoring, target marketing, churn prevention, forensic identification, medical diagnosis and fraud detection. The SAS ® System provides numerous tools that may be used for the prediction of both continuous and categorical response variables, targets in the current lexicon. While there are many different methodologies (neural networks, etc.) used for these purposes, in this presentation I will limit myself to prediction algorithms based on recursive partitioning, commonly called decision trees. There are several widely used tree based algorithms which exist and they are known by the names like; CART, C5.0 and CHAID, among others. Decision trees involve splitting the data into groups by successively dividing the data into subgroups based on empirically derived associations between the response (target) and one or more, usually many more, predictor variables. In that effort observations are sorted into bins based on the value(s) of the predictor(s). Criteria must be established for each predictor to determine which observations go in which bins in such a way as to maximize the association between the response and each predictor and then how decide which of the predictors has the best association with the target variable in the particular subgroup being divided. Of course “best” has to be defined in terms of some optimality criterion in order to make any sense. The following discussion will describe ways in which those decisions can be made and then ways in which the predictive algorithms, thus derived, can be validated. The simplest decision tree algorithms often do not perform well in comparison with other predictive modeling methods (regression, etc). Fortunately for everybody, their performance can usually be improved by implementation of some additional strategies. We will discuss some methods such as bootstrapping that often produce improved results compared to initial results.