# Tuning for hyperparameters, sample size and test-train performance with Sklearn

I'm performing model evaluation of my sklearn Pipeline, looking for:

• optimal hyperparameters: the model is a random forest so in this case: number of trees and tree max depth (using grid search)

• train size: size of the training set (using validation_curve)

• train/test performance: to check for high bias / high variance (using learning_curve)

My question is: In which order should I perform these steps?

Let say I start with train size, which hyperparameters should I use? Or if I start with hyperparamter tuning, should I use the whole dataset (it's huge) even if it takes forever to train?