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The recently published fourth Fermi Large Area Telescope source catalog(4FGL) reports 5065 gamma-ray sources in terms of direct observationalgamma-ray properties. Among the sources, the largest population is the ActiveGalactic Nuclei (AGN), which consists of 3137 blazars, 42 radio galaxies, and28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars(FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of anunknown type (BCUs). The classification of blazars is difficult using opticalspectroscopy given the limited knowledge with respect to their intrinsicproperties, and the limited availability of astronomical observations. Toovercome these challenges, machine learning algorithms are being investigatedas alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermiblazars with 23 parameters is systematically selected. Three establishedsupervised machine learning algorithms (random forests (RFs), support vectormachines (SVMs), artificial neural networks (ANNs)) are employed to generalpredictive models to classify the BCUs. We analyze the results for all of thedifferent combinations of parameters. Interestingly, a previously reportedtrend the use of more parameters leading to higher accuracy is not found.Considering the least number of parameters used, combinations of eight, 12 or10 parameters in the SVM, ANN, or RF generated models achieve the highestaccuracy (Accuracy $\simeq$ 91.8\%, or $\simeq$ 92.9\%). Using the combinedclassification results from the optimal combinations of parameters, 724 BL Lactype candidates and 332 FSRQ type candidates are predicted; however, 256 remainwithout a clear prediction.

Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning