Model Refinement
Model refinement is the process of improving the performance of a machine learning model by making adjustments to its parameters, hyperparameters, or architecture. It involves fine-tuning the model to achieve better accuracy, precision, recall, or F1 score. Model refinement can be done through various techniques such as cross-validation, regularization, feature selection, ensemble methods, and hyperparameter tuning. The goal of model refinement is to create a model that generalizes well to new data and is not overfitting or underfitting the training data. Model refinement is an iterative process that requires experimentation and evaluation of different configurations until the desired performance is achieved.
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