The correct answer is A. Model Optimization.
Model optimization is the process of finding the optimal parameters for a model to achieve better performance. This is done by iteratively adjusting the parameters of the model and evaluating the model’s performance on a holdout set of data. The goal of model optimization is to find the set of parameters that minimizes the error of the model on the holdout set.
Model evaluation is the process of assessing the performance of a model. This is done by comparing the model’s predictions to the actual values. The goal of model evaluation is to determine how well the model performs on unseen data.
Model training is the process of learning the relationship between the input and output variables of a model. This is done by feeding the model a set of training data and adjusting the model’s parameters until the model can accurately predict the output values for the training data.
Model selection is the process of choosing the best model from a set of models. This is done by evaluating the performance of each model on a holdout set of data and selecting the model that performs the best.
In conclusion, model optimization is the process of finding the optimal parameters for a model to achieve better performance. This is done by iteratively adjusting the parameters of the model and evaluating the model’s performance on a holdout set of data. The goal of model optimization is to find the set of parameters that minimizes the error of the model on the holdout set.