Table of difference between Model Parameters and HyperParameters
| PARAMETERS | HYPERPARAMETER |
|---|---|
| They are required for making predictions | They are required for estimating the model parameters |
| They are estimated by optimization algorithms(Gradient Descent, Adam, Adagrad) | They are estimated by hyperparameter tuning |
| They are not set manually | They are set manually |
| The final parameters found after training will decide how the model will perform on unseen data | The choice of hyperparameters decide how efficient the training is. In gradient descent the learning rate decide how efficient and accurate the optimization process is in estimating the parameters |
Difference Between Model Parameters VS HyperParameters
The two most confusing terms in Machine Learning are Model Parameters and Hyperparameters. In this post, we will try to understand what these terms mean and how they are different from each other.