  Many Machine Learning (ML) models are referred to as black box models,
providing no real insights into why a prediction is made. Feature importance
and explainability are important for increasing transparency and trust in ML
models, particularly in settings such as healthcare and finance. With quantum
computing's unique capabilities, such as leveraging quantum mechanical
phenomena like superposition, which can be combined with ML techniques to
create the field of Quantum Machine Learning (QML), and such techniques may be
applied to QML models. This article explores feature importance and
explainability insights in QML compared to Classical ML models. Utilizing the
widely recognized Iris dataset, classical ML algorithms such as SVM and Random
Forests, are compared against hybrid quantum counterparts, implemented via
IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum
Support Vector Classifier (QSVC). This article aims to provide a comparison of
the insights generated in ML by employing permutation and leave one out feature
importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley
Additive exPlanations) explainers.
