What is a Model Parameter?
A model parameter is a variable of the selected model which can be estimated by fitting the given data to the model.
Example:
In the above plot, x is the independent variable, and y is the dependent variable. The objective is to fit a regression line to the data. This line(the model) is then used to predict the y-value for unseen values of x. Here, m is the slope and c is the intercept of the line. These two parameters(m and c) are estimated by fitting a straight line to the data by minimizing the RMSE(root mean squared error). Hence, these parameters are called the model parameters.
Model parameters in different models:
- m(slope) and c(intercept) in Linear Regression
- weights and biases in Neural Networks
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.