## 2 Main MI Algorithms for Signal and Background Discrimination and Their Use at Particle Colliders
Moving on to the methodologies, as explained above, BDTs are among the most popular at LHC experiments. BDT stands for \"Boosted Decision Tree\", and is currently one of the most popular algorithms for classification in ML. BDTs are based on the combination of weak predictive models (decision trees or DTs) to create a stronger one. These DTs are generated sequentially, with each tree being created to correct the errors in the previous one. This process of sequencing is known as \"Boosting\", and can be of different types. The usual trees in BDTs are typically \"shallow\", which means they have just one, two, or three levels of deepness. BDTs were designed to improve the disadvantages of the regular DTs, such as as a tendency to overfit and their sensitivity to unbalanced training data. When boosting DTs, a very common choice is Adaptive Boosting (AdaBoost). This boosting technique is one of the canonical examples of ensemble learning in ML. In this method, the weights that are assigned to the classified instances are re-assigned at each step of the process. AdaBoost gives higher values to the more incorrectly classified instances in order to reduce bias and the variance. With this, one can can make weak learners, such as DTs, stronger. The main difference between other types of boosting and AdaBoost relates to the use of particular loss functions. BDTs boosted with AdaBoost are the most frequent algorithm used at the LHC for signal-background discrimination by means of ML. Due to this, in the first part of the paper, we focus on how the performance obtained by this algorithm depend on the library used (namely, TMVA vs. Sklearn). A very popular alternative in ML is NNs. This is a type of model that can try to emulate the behaviour of the human brain, whose potential for HEP was first introduced decades ago [33]. In NNs, nodes, usually denominated \"artificial neurons\", are connected to each other to transmit signals. The main goal is to transmit these signals from the input to the end in order to generate an output. The neurons of a network can be arranged in layers to form the NN. Each of the neurons in the network is given a weight, which modifies the received input. This weight changes the values that come through, to later continue on their way through the network. Once the end of the network has been reached, the final prediction calculated by the network is shown as the output. In general, it can be said that an NN is more complex the more layers and neurons it has. Although they are not directly part of this study, for reference, we now briefly review some of the algorithms that data scientists have developed more recently. Even though some of these are slowly being incorporated into HEP, they are beyond the scope of our analysis. One example of these new developments is that of Extreme Gradient Boosting (XGBoost) [34], which is yet another type of boosting. XGBoost modifies the more traditional BDT algorithms by using a different objective function, so that a convex loss function is combined with a penalty term that accounts for the model complexity. XGBoost can be used together with different types of techniques, such as independent component analysis, gray wolf optimization or the whale optimization algorithm, to improve the general BDT results [35; 36]. Going beyond this, we should now mention \"liquid learning\". Liquid Learning [37] is a new type of ML algorithm that continuously adjusts depending on new data inputs. This means that the algorithm is able to modify its internal equations do that it can always adapt to changes in the incoming data stream. The fluidity of this new type of learning makes the algorithms more robust against noise or unforeseen changes in the data. Finally, one of the most expected developments these days concerns quantum technologies, and, with these, quantum ML (QML) [38]. QML algorithms and models try to use the advantages of quantum technologies to improve classical ML, for instance, by developing efficient implementations of slow classical algorithms using quantum computing.