  Most representative decision tree ensemble methods have been used to examine
the variable importance of Treasury term spreads to predict US economic
recessions with a balance of generating rules for US economic recession
detection. A strategy is proposed for training the classifiers with Treasury
term spreads data and the results are compared in order to select the best
model for interpretability. We also discuss the use of SHapley Additive
exPlanations (SHAP) framework to understand US recession forecasts by analyzing
feature importance. Consistently with the existing literature we find the most
relevant Treasury term spreads for predicting US economic recession and a
methodology for detecting relevant rules for economic recession detection. In
this case, the most relevant term spread found is 3 month to 6 month, which is
proposed to be monitored by economic authorities. Finally, the methodology
detected rules with high lift on predicting economic recession that can be used
by these entities for this propose. This latter result stands in contrast to a
growing body of literature demonstrating that machine learning methods are
useful for interpretation comparing many alternative algorithms and we discuss
the interpretation for our result and propose further research lines aligned
with this work.
