The scores indicate the significance of each feature. A higher score means the specific feature will greatly impact the model used to predict a specific target. Figure shows the RFC computed FI as percentages. Here, we show only the feature FI greater than 1.8 %. It is clear that the number of unfilled orbitals, availability of unfilled D and F orbitals, and the magnetic moments of the elements are the main influencing features of the RFC model to determine whether a material is magnetic or not. The number of unfilled orbitals is considerably higher in transition metal and rare earth elements. The magnetic moments of the ground state and BCC structure are nonzero for most of the materials formed with those elements (i.e., pure Fe, Co, and so on). The availability of unfilled D and F orbitals indicates the presence of partially filled D and F orbitals. Figure shows the number of ternary cubic materials as a function of the maximum difference of unfilled orbitals for both nonmagnetic and magnetic classes. Based on our data, the nm: m ratio is 3.4:1 when the maximum difference of unfilled orbitals (MaxDiff Unfilled Orbs) is less than 11. If that quantity is greater than 11, the nm:m ratio becomes 1:3.4. Thus, there is a higher probability of finding a magnetic material compared to nonmagnetic materials if MaxDiff Unfilled Orbs is greater than 11. To train the DNN and RFC models, we randomly split the ternary cubic transition metal-based dataset into train and test subsets. The training subset contains 90 % of the 10,285 compositions, whereas the test subset has 10 %. The k-fold cross-validation is a technique utilized to assess the predicting ability of the model on new data. We used 3-fold cross-validation, which provides 0.885, 0.884, and 0.885 accuracies for each training step of DNN, while that of RFC are 0.915, 0.898, and 0.900. Thus, we got 0.88 ± 0.0005 and 0.90 ± 0.01 mean accuracy for the DNN and RFC models, respectively. Figure shows the normalized confusion matrices of the two machine learning models. After classifying the materials, 67 (65) % were identified as nonmagnetic materials, and 23 (22) % were predicted as magnetic materials by the DNN (RFC) models correctly. The false nonmagnetic and false magnetic materials are less than 8 % from both models. In the test dataset, ≈ 28 % of the data were magnetic materials, and ≈ 72 % of them were nonmagnetic materials. Therefore, the true nonmagnetic and true magnetic data have a good agreement with the percentages in the test dataset.