When using the NB theorem to estimate the distribution of microbes in water sources, one could apply either maximum posterior probability (evaluation metrics: RMSEc) or direct averaging posterior probability (evaluation metrics: RMSEp) . Direct averaging of the source posterior probability yields more precise source distribution estimates with RMSEc being significantly lower than RMSEp. The more precise source distribution estimates are because direct estimation bypasses the information loss that typically happens when frequencies are first classified and then averaged . SourceTracker as a ML tool estimates the proportion of contaminants . SourceTracker employs the Gibbs sampling technique within a Bayesian framework and is more efficient than both NB-and RFbased source tracking methods . The superior performance of SourceTracker is because it can handle ambiguity in the source and sink distributions and can model a sink sample as a blend of various sources. SourceTracker can track the origin of bacteria in drinking water and water sources . For instance, a study developed six ML models (XGBoost, KNN, NB, SVM, NN, and RF) to predict microbial contamination in a watershed in California using data on land cover, weather, and hydrologic variables . That study used SourceTracker to generate ground-truth data for training purposes. Out of the six models, XGBoost outperforms the other models in terms of accuracy and AUC (average AUC = 0.88) when tracking the primary sources of microbial contamination in the watershed.