Before we start, maybe it's better to explain why we call them hyperparameters and not parameters. In machine learning, model parameters can be learned from the data, which means that while you train your model, you fit the model's parameters. On the other hand, we usually set hyperparameters before we start training the model. In order to give an example, you can think of coefficients in regression models as model parameters. A hyperparameter example, we can say the learning rate in many different models or the number of clusters (k) in k-means clustering.
Another important thing is the relationship between model parameters and hyperparameters, and how they shape our machine learning model, in other words, the hypothesis of our model. In machine learning, parameters are used for configuring the model, and this configuration will tailor the algorithm...