Let us revisit the distinction between parameters and hyperparameters in ML models.

Model Parameters:

• Model parameters are variables that are internal to the model and whose values are estimated from the training data during the learning process. [1] [4] [7]
• They are required by the model to make predictions. [5]
• Examples of model parameters include the weights and biases in a neural network, coefficients in a linear regression or logistic regression model, and the support vectors in a support vector machine. [1] [5] [7]
• The final values of the parameters define the skill of the trained model. [5]
• Model parameters are not set manually by the practitioner. Their values are learned automatically by the machine learning algorithm during training. [4] [5]

Hyperparameters:

• Hyperparameters are settings that are external to the model and whose values cannot be estimated from the training data. [2] [4]
• They are set manually by the practitioner before the learning process begins. [1] [2] [3] [7]
• Hyperparameters are used to control the learning process and they have a significant impact on the performance of the model being trained. [2] [3] [4]
• Examples of hyperparameters include the learning rate, number of epochs, batch size, number of hidden layers and neurons in a neural network, regularization parameters, and choice of activation functions. [1] [2] [4] [7]
• The optimal values for the hyperparameters are found through hyperparameter tuning - searching different combinations to find the one that results in the best performing model. [3] [5]

In summary, model parameters are the variables that the machine learning model learns automatically from the training data, while hyperparameters are set manually to control the learning algorithm itself and cannot be learned directly from the data. Choosing the right hyperparameter values is crucial to optimizing model performance.

References

[1] datascience.stackexchange.com: What is the Difference Between Model Hyperparameters and Model Parameters?
[2] deepai.org: Hyperparameter Definition and Explanation
[3] aws.amazon.com: What is Hyperparameter Tuning?
[4] geeksforgeeks.org: Difference Between Model Parameters VS HyperParameters
[5] machinelearningmastery.com: What is the Difference Between a Parameter and a Hyperparameter?
[6] baeldung.com: Features, Parameters, and Classes in Machine Learning
[7] towardsdatascience.com: Model Parameters and Hyperparameters in Machine Learning — What is the difference?
[8] wikipedia.org: Hyperparameter (machine learning)
[9] reddit.com: What are Parameters and Why Do They Matter?
[10] youtube.com: Hyperparameters and Parameters in Machine Learning and Deep Learning
[11] towardsdatascience.com: Parameters and Hyperparameters in Machine Learning
[12] deepchecks.com: What are Model Parameters?
[13] javatpoint.com: Hyperparameters in Machine Learning
[14] javatpoint.com: Difference between Model Parameter and Hyperparameter
[15] techopedia.com: What is a Hyperparameter (ML Hyperparameter)?

Based on a chat with claude-3-opus on perplexity.ai