A glass-box classifier. Pick a hierarchical dataset, watch agglomerative clustering build an ultrametric tree in real time, read the cophenetic loss, and click any leaf to see — in plain steps — why it was classified where it was.
The classifier builds a hierarchical tree by repeatedly merging the two closest clusters (agglomerative clustering). The height at which two points first merge is their cophenetic distance — the tree's internal measure of similarity. A high CPCC (close to 1) means the tree faithfully preserves the original geometry.
CPCC = Pearson corr( d_ij , c_ij )The LinMap view on the right embeds each point at the merge heights along its path to the root — an ultrametric (tree) coordinate system. This is the "glass box": every classification has a visible, inspectable structure, unlike a weight matrix.
Press Train Neural Baseline to fit a small linear classifier on the same labeled points. It reaches an accuracy score but gives you only probabilities — no reason, no path, no hierarchy.
The tree gives an interpretable merge history for every leaf. That is the glass-box property the Q-PNA approach is designed to expose: why a point is grouped with its neighbors, step by step.
† Pedagogical model: demonstrates interpretable hierarchical (ultrametric) classification and compares it with a simple neural baseline. It is not a claim about proprietary Q-PNA software or physical hardware. See QWAV Research Specification (Q-PNA v2.0) for the underlying method.