What is the difference between classifiers and models?
I'm trying to understand what a model and what a classifier is in machine learning. It is my current understanding that classifiers are things like linear regression, neural network and decision trees.
Thank you for your question!
A classifier/regressor/clustering algorithm refers to the type of machine learning algorithm you have chosen to work with - Linear regression, Logistic regression, K-Means clustering, etc.
This becomes a model once you have set the hyperparameters of your classifier during the training/validation process.
For example, let's assume you have a clustering problem and have decided to use K-Means clustering to solve it. Once you have chosen the value of K that works best for your problem (and maybe the values of other parameters that you can fine-tune), then you have created a model. You then test your model on the test dataset and evaluate its performance.
Hope this helps!