The classification reports of the two machine learning models are given in Table . Precision indicates the quality of a positive prediction made by the model. Precision is given by the number of true positives divided by the total number of positive predictions. The DNN and RFC models exhibit 0.93 (0.82) and 0.92 (0.84) precision for the nonmagnetic (magnetic) materials, respectively. The recall is computed as the ratio between the number of positive samples accurately categorized as positive to the total number of Positive instances in the test dataset. The recall from the DNN model is 0.93 (0.81), and that from the RFC model is 0.93 (0.81) for the nonmagnetic (magnetic) compositions. The weighted average of precision and recall is given the F1-score, which is 0.93 (0.81) from the DNN model and 0.95 (0.74) from the RFC model for the nonmagnetic (magnetic) compositions. Finally, the accuracy of the models was calculated as the total number of correctly predicted samples over the total number of samples. It was found that the accuracy of the DNN model for the test set is 0.90 and that for the training set is 0.93. However, the accuracy of the RFC model is 0.91 for the test set and ≈ 1 for the train set. As a result of having an accuracy ≈ 1 for the training set, the RFC model can show poor performance on the new data samples. Therefore, we used the DNN model for predicting the new spintronic materials.