Model Training
Model training is the process of feeding data to a machine learning algorithm to enable it to learn and make predictions or decisions. The goal of model training is to optimize the model's parameters so that it can accurately predict outcomes for new, unseen data. This process involves selecting appropriate features, preparing the data, choosing a suitable algorithm, and tuning the hyperparameters. The model is then trained on a training dataset, and its performance is evaluated on a validation dataset. The training process is repeated until the model achieves satisfactory performance on the validation dataset. Once the model is trained, it can be used to make predictions on new data.
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