I am working on a multiclass classification problem. I want to know whether my model is overfitting or underfitting. I am learning how to plot learning curves. My question is, is the order of steps I have done correct?
- Scaling
- Baseline model
- learning curve to see how well baseline model performs
- Hyperparameter tuning
- Fit the model and predict on test data
- Final learning curve to determine if the model is over or under fitting
The first plot is after I do CV for baseline model and before hyperparameter tuning, and the second plot is done at the end, after hyperparameter tuning and fitting the best hyperparameters to the final model
