  This paper develops an approach to classify instances of product failure in a
complex textiles manufacturing dataset using explainable techniques. The
dataset used in this study was obtained from a New Zealand manufacturer of
woollen carpets and rugs. In investigating the trade-off between accuracy and
explainability, three different tree-based classification algorithms were
evaluated: a Decision Tree and two ensemble methods, Random Forest and XGBoost.
Additionally, three feature selection methods were also evaluated: the
SelectKBest method, using chi-squared as the scoring function, the Pearson
Correlation Coefficient, and the Boruta algorithm. Not surprisingly, the
ensemble methods typically produced better results than the Decision Tree
model. The Random Forest model yielded the best results overall when combined
with the Boruta feature selection technique. Finally, a tree ensemble
explaining technique was used to extract rule lists to capture necessary and
sufficient conditions for classification by a trained model that could be
easily interpreted by a human. Notably, several features that were in the
extracted rule lists were statistical features and calculated features that
were added to the original dataset. This demonstrates the influence that
bringing in additional information during the data preprocessing stages can
have on the ultimate model performance.
