  The recently published fourth Fermi Large Area Telescope source catalog
(4FGL) reports 5065 gamma-ray sources in terms of direct observational
gamma-ray properties. Among the sources, the largest population is the Active
Galactic Nuclei (AGN), which consists of 3137 blazars, 42 radio galaxies, and
28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars
(FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of an
unknown type (BCUs). The classification of blazars is difficult using optical
spectroscopy given the limited knowledge with respect to their intrinsic
properties, and the limited availability of astronomical observations. To
overcome these challenges, machine learning algorithms are being investigated
as alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermi
blazars with 23 parameters is systematically selected. Three established
supervised machine learning algorithms (random forests (RFs), support vector
machines (SVMs), artificial neural networks (ANNs)) are employed to general
predictive models to classify the BCUs. We analyze the results for all of the
different combinations of parameters. Interestingly, a previously reported
trend the use of more parameters leading to higher accuracy is not found.
Considering the least number of parameters used, combinations of eight, 12 or
10 parameters in the SVM, ANN, or RF generated models achieve the highest
accuracy (Accuracy $\simeq$ 91.8\%, or $\simeq$ 92.9\%). Using the combined
classification results from the optimal combinations of parameters, 724 BL Lac
type candidates and 332 FSRQ type candidates are predicted; however, 256 remain
without a clear prediction.
