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State-of-the-art planet formation models are now capable of accounting forthe full spectrum of known planet types. This comes at the cost of increasingcomplexity of the models, which calls into question whether established linksbetween their initial conditions and the calculated planetary observables arepreserved. In this paper, we take a data-driven approach to investigate therelations between clusters of synthetic planets with similar properties andtheir formation history. We trained a Gaussian Mixture Model on typicalexoplanet observables computed by a global model of planet formation toidentify clusters of similar planets. We then traced back the formationhistories of the planets associated with them. Using cluster affiliation aslabels, we trained a Random Forest classifier to predict planet species fromproperties of the originating protoplanetary disk. Without presupposing anyplanet types, we identified four distinct classes in our synthetic population.They roughly correspond to the observed populations of (sub-)Neptunes, giantplanets, and (super-)Earths, plus an additional unobserved class we denote as"icy cores". These groups emerge already within the first 0.1 Myr of theformation phase and are predicted from disk properties with an overall accuracyof >90%. The most reliable predictors are the initial orbital distance ofplanetary nuclei and the total planetesimal mass available. Giant planets formonly in a particular region of this parameter space that is in agreement withpurely analytical predictions. Including N-body interactions between theplanets decreases the predictability, especially for sub-Neptunes thatfrequently undergo giant collisions and turn into super-Earths. The impact ofgravitational interactions highlights the need for N-body integrators forrealistic predictions of systems of low-mass planets. (abridged)

The New Generation Planetary Population Synthesis (NGPPS). V. Predetermination of planet types in global core accretion models