Training Process
Training process refers to the iterative process of feeding data into a machine learning algorithm to optimize its performance. The process involves selecting appropriate features, preparing the data, selecting a model, and tuning hyperparameters to achieve the best possible performance. The training process is typically divided into two phases: training and validation. During the training phase, the algorithm is fed with labeled data and learns to make predictions. In the validation phase, the algorithm is tested on a separate set of data to evaluate its performance and prevent overfitting. The training process may be repeated multiple times with different parameters and data subsets to improve the model's accuracy and generalization ability.
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