# Applying Machine Learning to Preselective Weighing of Moving Vehicles
## 3 Examples of Implementations of Machine Learning Algorithms  
Simple examples of machine learning (ML) applications include intelligent spam classifiers, which protect email by learning from vast amounts of data considered spam and user feedback to classify email messages. Another example is advertising systems that learn to match the content of ads displayed on the Internet to the context of a website and the interests of its visitors.  
These examples could be multiplied; however, two critical factors enable the successful implementation of machine learning algorithms:  
1. The use of effective statistical models that accurately capture complex data relationships;
2. Scalable learning systems capable of training models on large datasets.  
One of the most effective machine learning methods used in practice is gradient tree boosting [22]. A study [23] demonstrated that the gradient tree boosting method produces state-of-the-art results on many standard classification benchmarks.  
This paper uses the scalable machine learning system XGBoost, which is commonly employed to enhance the number of gradient-boosted regression trees. In addition to serving as a standalone predictor, XGBoost is incorporated into real-world data pipelines, such as predicting ad click-through rates [24].  
XGBoost's scalability is attributed to several key system and algorithmic optimizations. These innovations include the following:  
* A novel tree-learning algorithm for handling sparse data;
* Parallel and distributed processing accelerates learning and allows the system to run more than ten times faster than existing popular solutions on a single machine;
* Scalability to billions of examples in distributed or memory-constrained environments.  
An example of the application of the XGBoost algorithm is presented in [25], where it is combined with the Grid Importance Rank method to analyze and select key areas for air pollution control. In this method, when analyzing air pollution data, the key challenge is identifying the factors that most impact pollution levels. The XGBoost algorithm evaluates and ranks the importance of features in areas targeted for environmental control. The algorithm predicts future air pollution levels by analyzing historical data, including pollution measurements, meteorological data, and traffic or industrial activity information. By understanding the importance of features, the variables contributing most to increased pollution levels can be identified. This analysis helps provide better insight into environmental and social factors, enabling targeted actions, such as reducing emissions in specific economic sectors. The implementation of machine learning algorithms has many applications across industries. However, it is an iterative process requiring relevant data and careful selection of machine learning models, which is crucial for their practical application.  
The next article [26] presents a more detailed review of the most important aspects of deep learning, including the latest developments in the field. In addition, this paper discusses the importance of deep learning and various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be used.  
An interesting application of the XGBoost and LightGBM algorithms was presented in the article [27]. The authors employ several spectral data preprocessing methods and feature-selection algorithms to enhance the model performance. Our results showed that the boosting algorithms generally outperformed the PLSR (partial least squares regression) models in terms of prediction accuracy. Additionally, the boosting algorithms with feature selection exhibited superior performance compared to the PLSR models in the majority of cases.