{"id":47722,"date":"2024-04-15T22:30:51","date_gmt":"2024-04-15T22:30:51","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=47722"},"modified":"2024-04-15T22:30:51","modified_gmt":"2024-04-15T22:30:51","slug":"in-machine-learning-what-is-the-term-for-the-process-of-finding-the-optimal-parameters-for-a-model-to-achieve-better-performance","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/in-machine-learning-what-is-the-term-for-the-process-of-finding-the-optimal-parameters-for-a-model-to-achieve-better-performance\/","title":{"rendered":"In machine learning, what is the term for the process of finding the optimal parameters for a model to achieve better performance?"},"content":{"rendered":"

[amp_mcq option1=”Model Optimization” option2=”Model Evaluation” option3=”Model Training” option4=”Model Selection” correct=”option1″]<\/p>\n

The correct answer is A. Model Optimization.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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