tokens: criterion, and a maximum of five features, as this set of hyperparameters performed best for V19 (see also Appendix D ). After training the random forest on the oversampled training set, we apply it to the 10 sets of features (from the 10 random posterior draws) for each transient in the test set to get 10 sets of classification probabilities. We then average the 10 sets of classification probabilities for each transient and adopt the classification with the highest probability. 4. Results and Validation Applying our classification pipeline to the test set described in Section 2 yields 1257 photometrically classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. 26 26 To exactly reproduce our results, a seed of 0 must be used in the pseudorandom number generators during feature extraction, oversampling, and classification. Table 3 gives the full list of classifications, and Figure 5 shows a sample of photometrically classified light curves. These are among the largest samples of each of these classes of SNe in the literature from a single survey. In the remainder of this section, we assess the performance of our algorithm in general and discuss how to use these classifications in practice. Figure 5. Sample observed (circles) and model (lines) light curves of photometrically classified SNe. Downward-pointing arrows indicate 3 σ upper limits from nondetections. The left column shows the highest confidence classification in each class, and the right column shows the median-confidence classification. Notice that in three of the lower confidence classifications, the light curve is poorly sampled on either the rise or the decline because it was discovered close to the boundaries of the observing season. Table 3 Classification Results (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. 4.1. Cross-validation We first validate our classifier using leave-one-out cross-validation with the SNe in our training set. For each iteration of the cross-validation, we retrain the classifier on all but one of the SNe in our training set (still using the median parameters) and then use it to classify 10 sets of features derived from the posterior parameter distributions for that SN. We then average the 10 sets of probabilities to determine the cross-validation classifications listed in Table 4 . Figure 6 shows the resulting confusion matrices. Figure 6. Confusion matrices of our validation results. Each cell lists and is colored by the fraction of each class, with the raw number in parentheses. The top matrix aggregates by true label, so its diagonal shows completeness. The middle matrix aggregates by predicted label, so its diagonal shows purity. The bottom matrices are the same results but with all four non-SN Ia classes grouped together as CCSNe. N is the total size of the training set, A is the
ner_tags: O O O O O O O O O O O O O O O B-Citation O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O