## 1 Introduction  
The wire monopole antenna is one of the most popular antennas because it provides an omni-directional radiation pattern, high radiation efficiency, and easy fabrication, etc. [1, 2, 3]. In addition, the geometry of a wire monopole antenna can be easily modified in various manners to improve antenna performances: control of operating frequency bands by adding resonating elements [4, 5], increment of antenna bandwidth by applying the tapered structure for a feeder [6], enhancement of the radiation gain by equipping with director and reflector wires [7, 8], reduction of antenna size by folding the antenna body [9, 10, 11], achievement of favorable impedance matching by placing shorting wires or capacitive loads at the optimum location of an antenna body [9, 12, 13, 14], and control of both radiation pattern and polarization by rendering antenna arms bent or folded [15, 16, 17]. Among the modified wire monopole antennas, the bent wire antennas such as an inverted-L antenna have good performances in the reduction of antenna size, as well as impedance matching because the angles between wire elements (bending angles) and the lengths of the wire elements affect the antenna impedance and the current induced on the wire [3, 9, 17, 18]. However, as is well known, the reduction of the antenna size simultaneously yields the increase in quality (\(Q\)) factor resulting in the diminishment of the antenna bandwidth [19, 20]. This is because the antenna bandwidth is inversely proportional to the \(Q\) factor. In the case of an inverted-L antenna, the location of a bending point on the wire monopole significantly influences the \(Q\) factor because the location of the bending point adjusts the antenna size--the smallest sphere enclosing the antenna structure. In the results of [9], as the height of the inverted-L antenna--which implies the distance between the bending point and the ground plane--decreases under 10% of an electrical wavelength (0.1 \(\lambda_{0}\)) at the operating frequency (\(f_{0}\)), the \(Q\) factor drastically increases. Thus, the bent wire antennas such as the inverted-L antenna have to be optimized by utilizing the useful methodology to achieve the required antenna bandwidth in the restricted antenna size. To optimize antenna structure having target antenna performances, various optimization algorithms such as the genetic algorithm and the particle swarm algorithm have been employed [21, 22]. The more complex the antenna structure is, the more effective the optimization algorithm is in antenna design. However, electromagnetic (EM) analysis or simulation required to evaluate the fitness of sample antennas in the optimization process is a burden to antenna designers because the EM simulation needs lots of time and efforts to obtain the results. To overcome the aforementioned limitation in antenna design, machine learning (ML) techniques can be one of the solutions because the resulting ML model approximately provides the proper structure of an antenna without the EM simulation [23, 24, 25]. In [23], machine learning (ML) techniques such as least absolute shrinkage and selection operator (lasso), artificial neural networks (ANNs), and k-nearest neighbor (kNN) provide an efficient framework to identify optimal design for a T-shaped monopole antenna. Machine learning [26] is a science that utilizes the capability of a computer to help humans deal with heterogeneous problems without being explicitly programmed. Some large projects related to machine learning which gained a big reputation in recent years can be listed here: GoogleBrain which focused on pattern detection in images and videos [27], AlexNet which led to the use of GPUs and convolutional neural networks in machine learning [28, 29, 30], and DeepFace which can recognize people with the same precision as a human can [31], etc. Supervised and unsupervised learning are most common machine learning methods. Supervised learning algorithms are trained by using the available input and the corresponding label (or the desired output).