 For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations or observations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths . As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set .
<clarity>  For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations or observations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths . As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set .
 For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations . Stable paths in the Mapper from a supervised machine learning model trained on the FashionMNIST data set .