  Complex prediction models such as deep learning are the output from fitting
machine learning, neural networks, or AI models to a set of training data.
These are now standard tools in science. A key challenge with the current
generation of models is that they are highly parameterized, which makes
describing and interpreting the prediction strategies difficult. We use
topological data analysis to transform these complex prediction models into
pictures representing a topological view. The result is a map of the
predictions that enables inspection. The methods scale up to large datasets
across different domains and enable us to detect labeling errors in training
data, understand generalization in image classification, and inspect
predictions of likely pathogenic mutations in the BRCA1 gene.
