Evaluate results on validation set

- [Instructor] Now that we've done some … hyper parameter tuning, … and we have a good idea of what the best … hyper parameter combinations are, … let's evaluate these models on some unseen data … in the validation set. … Now the performance shouldn't deviate too much … from the performance we saw with the cross validation, … because the performance metrics there … were also on unseen data. … But this will give us the opportunity to look at a couple … additional performance metrics … beyond just accuracy. … So I do want to mention at this point … that typically you'll test different algorithms as well. … In this case, we're only using random force classifier … just to keep things simpler. … But usually we would have many different … candidate models to choose from. … In this lesson, we're going to take those three … best performing models from the last lesson … and really dig in to understand how they're performing. … You'll notice that we're importing … a couple additional metrics … that we did not import last time. …