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Magnetars are usually interpreted as highly magnetized neutron stars, yet a small subset of low spin-down sources has motivated alternative scenarios involving highly magnetized white dwarfs. We test whether the observed magnetar sample is consistent with a single neutron-star population or whether the data favor an additional compact-object channel. We combine exploratory machine-learning diagnostics with hierarchical Bayesian population modeling. First, we apply K-means clustering and principal component analysis for visualization in a five-dimensional feature space <mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>P</mml:mi><mml:mo>,</mml:mo><mml:mover><mml:mi>P</mml:mi><mml:mo>˙</mml:mo></mml:mover><mml:mo>,</mml:mo><mml:msub><mml:mi>L</mml:mi><mml:mi>X</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mo>|</mml:mo><mml:mi>Z</mml:mi><mml:mo>|</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:math>, where P is the spin period, <mml:math><mml:mover><mml:mi>P</mml:mi><mml:mo>˙</mml:mo></mml:mover></mml:math> its time derivative, L<SUB>X</SUB> the X-ray luminosity, kT the thermal spectral temperature, and |Z| the absolute Galactic scale height, and then train a Random Forest classifier with leave-one-out cross-validation to identify the observables driving the empirical split. We subsequently construct a hierarchical Bayesian mixture model that links spin parameters to magnetic-field distributions through covariate-dependent mixing fractions. Posterior inference is performed with Hamiltonian Monte Carlo, and predictive performance is assessed with Pareto-smoothed importance sampling leave-one-out cross-validation. The exploratory analysis reveals a reproducible sub-structure: the Random Forest reaches &gt; 95% LOOCV accuracy, with L<SUB>X</SUB>, <mml:math><mml:mover><mml:mi>P</mml:mi><mml:mo>˙</mml:mo></mml:mover></mml:math>, and kT emerging as the dominant predictors. However, the Bayesian comparison shows no statistically significant preference for a two-population model. Instead, a few low spin-down sources receive intermediate posterior membership probabilities, indicating that they are better interpreted as transitional or outlying objects than as members of a clearly distinct class. Overall, current data do not require a separate white-dwarf magnetar population. The main result is therefore conservative but strong: the observed sample is adequately described by a predominantly neutron-star population, while still allowing physically interesting deviations in specific sources.