3rd ICAI 2024

International Conference on Automotive Industry 2024

Mladá Boleslav, Czech Republic

• Unsupervised Learning: Unlike supervised learning, unsupervised learning involves training the algorithm on an unlabelled dataset. The algorithm must identify patterns or structures within the data without explicit guidance on the correct output. Clustering and dimensionality reduction are typical tasks in unsupervised learning, where the algorithm aims to uncover hidden relationships or groupings in the data. • Reinforcement Learning: Reinforcement learning operates on the principle of learning by interacting with an environment. The algorithm, called an agent, learns to achieve a goal through a process of trial and error, receiving feedback in the form of rewards or penalties based on its actions. Over time, the agent learns to make decisions that maximize cumulative rewards. Reinforcement learning is commonly used in fields such as robotics, gaming, and autonomous systems.

Figure 2: Machine learning business application deployment scheme

Source: Own elaboration

2.2.1 Extra Trees Regressor Extra Trees Regressor is an ensemble learning algorithm that belongs to the family of decision tree-based models, like Random Forests. It is a variant of the Random Forest algorithm and is particularly powerful for regression tasks. Characterization of the Extra Trees Regressor: • Ensemble Learning: The Extra Trees Regressor utilizes ensemble learning, where several decision trees are trained autonomously and subsequently amalgamated to formulate predictions. This ensemble methodology frequently yields superior performance when juxtaposed with individual models. • Randomization: What distinguishes Extra Trees from conventional decision trees is its heightened randomization during the construction of each decision

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