Model Optimization
Model optimization is the process of adjusting the parameters of a machine learning model to achieve the best possible performance on a given task. This involves selecting the appropriate algorithm, tuning hyperparameters, and optimizing the model architecture. The goal of model optimization is to improve the accuracy, speed, and generalization of the model, while avoiding overfitting or underfitting. Model optimization can be performed using various techniques such as grid search, random search, Bayesian optimization, and gradient-based optimization. It is an iterative process that requires careful experimentation and evaluation of the model's performance on a validation set. Model optimization is a crucial step in the machine learning pipeline, as it can significantly impact the quality of the results and the efficiency of the system.
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