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In this article, we explore the potential of artificial neural networks,which are trained using an exceptionally simplified catalog of idealconfigurations encompassing both order and disorder. We explore thegeneralisation power of these networks to classify phases in complex modelsthat are far from the simplified training context. As a paradigmatic case, weanalyse the order-disorder transition of the diluted Ising model on severaltwo-dimensional crystalline lattices, which does not have an exact solution andpresents challenges for most of the available analytical and numericaltechniques. Quantitative agreement is obtained in the determination oftransition temperatures and percolation densities, with comparatively much moreexpensive methods. These findings highlight the potential of minimalisttraining in neural networks to describe complex phenomena and have implicationsbeyond condensed matter physics.

Minimalist Neural Networks training for phase classification in diluted-Ising models