Trénink (or „training“ in English) in the context of computer science refers to the process of teaching a machine learning model to recognize patterns and make predictions based on data. This involves using a dataset to adjust the model’s parameters so that it can perform a specific task, such as classification, regression, or clustering. The training process typically consists of feeding the model input data along with corresponding labels (in supervised learning), allowing the model to learn the relationships within the data. The model uses algorithms to minimize the difference between its predictions and the actual outcomes, effectively optimizing its performance over time. Training can also involve techniques such as cross-validation, hyperparameter tuning, and regularization to enhance the model’s accuracy and generalization to unseen data. The quality of the training data and the choice of algorithms significantly impact the success of the trained model in real-world applications.
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