  The deepest all-sky survey available in the $\gamma$-ray band - the last
release of the Fermi-LAT catalogue (4FGL-DR3) based on the data accumulated in
12 years, contains more than 6600 sources. The largest population among the
sources is blazar subclass - 3743, $60.1\%$ of which are classified as BL
Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs), while the
rest are listed as blazar candidates of uncertain type (BCU) as their firm
optical classification is lacking. The goal of this study is to classify BCUs
using different machine learning algorithms which are trained on the spectral
and temporal properties of already classified BL Lacs and FSRQs. Artificial
Neural Networks, \textit{XGBoost} and LightGBM algorithms are employed to
construct predictive models for BCU classification. Using 18 input parameters
of 2219 BL Lacs and FSRQs, we train (80\% of the sample) and test (20\%) these
algorithms and find that LightGBM model, state-of-the-art classification
algorithm based on gradient boosting decision trees, provides the highest
performance. Based on our best model, we classify 825 BCUs as BL Lac candidates
and 405 as FSRQ candidates, however, 190 remain without a clear prediction but
the percentage of BCUs in 4FGL is reduced to 5.1\%. The $\gamma$-ray photon
index, synchrotron peak frequency, and high energy peak frequency of a large
sample are used to investigate the relationship between FSRQs and BL Lacs
(LBLs, IBLs, and HBLs).
