Resolved: Standalone/baseline model
At the beginning of the lesson, Mrs. Lauren mentioned that the Logistic model is a great model to use either as a standalone or baseline model. The question here is, what is the difference between standalone and baseline models?
Could provide examples, please...
Hi Abdulrahman!
A standalone model is a model that is used on its own to make predictions. This is the final model you choose for your task because it performs well enough and you don't need to combine it with other models. For example, if you train a logistic regression model and it achieves 95% accuracy, that's your standalone model because it works well enough.
A baseline model is a model used for comparison. It helps you see whether more complex models are really doing better than something basic. For example, you can train a logistic regression model beause it's relatively easy but you see it doesn't work perfectly. So, you decide to try with more complex models like Random forest or XGBoost. Then, you compare their performance to the logistic model, which is, in this case, your baseline model.
If it turns out the logistic regression performs better after all, it becomes the standalone model.
So, here's how a logistic model could be both a standalone and a baseline model.
Hope this helps.
Best,
Ivan