Estimating from No Data: Deriving a Continuous Score from Categories

A technical walkthrough explains how to derive a continuous, interpretable score from a neural network trained only on categorical labels, using a low-capacity linear bottleneck plus a 'category approximator head' with trainable thresholds. It covers the math for extracting thresholds from a softmax dense layer's weights and biases, a 'propagate-sum' trick using Keras tensor slicing to force category ordering, and demonstrates the approach on a toy medical dataset predicting infection severity from 8 features, achieving 80% accuracy with a fully interpretable linear model.

Source: https://towardsdatascience.com/estimating-from-no-data-deriving-a-continuous-score-from-categories-2. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.