I am working on a school project using remote sensing data, for classification purposes. And I am trying to select the best model (models) for my data. The approach that I adopted is the following:
- Looping over different models and using K-fold cross-validation (k=10).
- Select the best performing model (models)
- Parameter grid search (or random search) for each of the models I chose in (2)
- Cross-validation using the best parameters I found in (3)
- Perform prediction
Does this approach make sense? I am still in step 1 and it is already taking up so much time. I am new to machine learning, so I wanted to get some insight into how people in this domain usually tackle projects. Any advice would be highly appreciated.