I've made a model (say Linear Regression) using a visual recipe and randomly split my data into 0.8 and 0.2 ratio for train and validation/test data. Now, after the model has been trained, I get different error metrics, R2 Score, scatter plot, and distribution of errors. I have the following questions-
1) These analyses are on the train data, not on validation/test data?
2) How can I check the error metrics on test data if the above-shown metrics are on the train data?
3) If I want to use a few more training parameters for modeling than it is available for any model by default, is there any plugin to do that visually?
Note: I could write a pyspark query and get all things done but the aim here is to deliver the solution with visually available recipes, which is much more friendly to use.
As answered on your subsequent post, all of these questions are clarified in our docs and training materials. Since your question covers several stages of the ML process, I recommend that you have a look at the materials in question here