  The field of radio astronomy is witnessing a boom in the amount of data
produced per day due to newly commissioned radio telescopes. One of the most
crucial problems in this field is the automatic classification of extragalactic
radio sources based on their morphologies. Most recent contributions in the
field of morphological classification of extragalactic radio sources have
proposed classifiers based on convolutional neural networks. Alternatively,
this work proposes gradient boosting machine learning methods accompanied by
principal component analysis as data-efficient alternatives to convolutional
neural networks. Recent findings have shown the efficacy of gradient boosting
methods in outperforming deep learning methods for classification problems with
tabular data. The gradient boosting methods considered in this work are based
on the XGBoost, LightGBM, and CatBoost implementations. This work also studies
the effect of dataset size on classifier performance. A three-class
classification problem is considered in this work based on the three main
Fanaroff-Riley classes: class 0, class I, and class II, using radio sources
from the Best-Heckman sample. All three proposed gradient boosting methods
outperformed a state-of-the-art convolutional neural networks-based classifier
using less than a quarter of the number of images, with CatBoost having the
highest accuracy. This was mainly due to the superior accuracy of gradient
boosting methods in classifying Fanaroff-Riley class II sources, with
3$\unicode{x2013}$4% higher recall.
