## 1 Introduction  
Over the past few years, the number and diversity of electrical equipment, such as motors, transformers, generators, electric vehicles, and energy transmission and distribution systems, among many others, are getting bigger [1; 2; 3; 4; 5; 6]. Their exponential growth is due to the need of people to perform a number of different activities, ranging from industrial processes to everyday activities such as charging the cell phone battery or starting the car to go to work. Due to their paramount importance in any facet of society, their safety and correct operation is vital, even more so when considering that a failure in one of its components can produce (1) high economical losses derived from its partial or total repair, (2) degradation and poor quality on its performance, (3) outages in the production process, (4) damages to other equipment, and (5) conditions that put in risk the physical integrity of people, among others. In this regard, the application and development of new techniques and methods to monitor the condition of electric machines and systems are important topics of research. In general, a condition monitoring strategy consists of the following steps (see Figure 1): Data collection through different types of sensors, data processing and feature extraction, and data analysis for condition assessment. The latter can be seen as the process of exploring, finding, selecting, and using specific data to solve the given problem, e.g., a diagnosis problem; however, it is not an easy and straightforward process since the data analyst has to deal with different volumes and varieties of data, as well as redundant and unneeded data, which can compromise and difficult the solution of the assigned task; in fact, the reality is that, in many cases, only a small part of the dataset is used because its volume is simply too large to be used and processed effectively. One solution to this problem has been the use of data mining (DM) techniques. DM is one of the fastest growing fields at both the computational and industrial levels. Its main characteristic involves the search of patterns through the handling of different sets of data to discover the available knowledge. [PERSON] [7] calls DM to the process of applying a computer-based methodology for discovering knowledge from data. Although DM is based on computational algorithms, best results can be obtained by balancing the knowledge of human experts about the problem under study with the advantages and operating modes of different algorithms [4]. In general, DM functionalities can be divided into two categories: Predictive and descriptive. The former is used to construct models that allow the prediction of unknown or future values, whereas the latter is in charge of finding new information that allows the description of the dataset. In this regard, the prediction functionalities become the most suitable option to perform the condition monitoring since a new and unknown equipment condition can be determined or predicted from a specific input information. Therefore, this manuscript is aimed at reviewing the classification and regression tasks that fall within the predictive category of DM, as well as hybrid techniques that combine more than one prediction method. Specifically, classification techniques attempt to find a function or model that distinguishes or predicts the class of unknown data by analyzing a data training set [8]. The regression analysis is used for numerical prediction, i.e., to predict missing or new numerical data values [8]. In the literature, two main groups of research works related to DM and electric equipment are found. On the one hand, there are different reviews about DM applications, e.g., diagnosis in health [9; 10; 11], marketing [12], industrial [13; 14], climatological [15], and financial [16] issues, among others.