Model Parameters

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Model Parameters

Model hyperparameters

In a machine learning model, there are two types of parameters:

a) Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. For example, suppose we have a model such as house price = a + b*(age) + c*(size), to estimate the cost of houses based on the age of the house and its size (square foot), then a, b, and c will be our model or fitted parameters.

b) Hyperparameters: These are adjustable parameters that must be tuned to obtain a model with optimal performance. 

It is important that during training, the hyperparameters be tuned to obtain the model with the best performance (with the best-fitted parameters).

Source : kdnuggets