I'm implementing Naive Bayes and a Decision Tree on the same data, and I need to cross validate with Kfold.
Do I have the right sequence of events? Overall, does it make sense or am I misunderstanding something? Some of what I've read is contradictory.
- Train_test_split (75/25 or whatever)
- Fit the model with X, y Training data
- Predict the model with X Test data
- cross_val_score using K(5)fold validation using Training data (and not the whole dataset) to see how good the model can fit.
- Confusion matrix on y_test, y_pred (y_pred is output from #3)
- Classification report on y_test, y_pred (y_pred is output from #3)
- Display the model with training data and display it again with test data to visualize the two results.