Question

What is the difference between Hyperparameters and model parameters?​

Answer 1

Answer:

Explanation:

Hi✌️✌️

Ek baat samajh nhi-tumhare question ka koi proper answer kyo nhi detta..xd

A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data.

▪️They are often used in processes to help estimate model parameters.

▪️They are often specified by the practitioner.

▪️They can often be set using heuristics.

▪️They are often tuned for a given predictive modeling problem.

Model parameters =>A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data.

▪️They are required by the model when making predictions.

▪️They values define the skill of the model on your problem.

▪️They are estimated or learned from data.

▪️They are often not set manually by the practitioner.

▪️They are often saved as part of the learned model.

I hope it will help you ^_^

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Answer 2

HERE IS YOUR ANSWER MATE:-

1. Hyperparametrs:- In machine learning, A hyperparametrs is a paramter whose values Is set before the learning process begins,Given these hyperparametrs ,the training algorithm learns the parameter from the data.
2. Model parameters:- A model parameter is a configuration variable that is internal to the model and whose value can be estimated from the data. They are required by the model when making predictions. They values define the skill of the model on your problem

Hope this helps u dear mate!!

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