From the course: Vibe Coding for Data Analysts
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Tuning models
From the course: Vibe Coding for Data Analysts
Tuning models
- Now we can turn the dials on our model to get the best vibes possible. With our baseline model, we need to spend some time tuning the hyper parameters. For our cross validation approach, we could choose to use a grid search, or a randomized search instead of making that choice explicitly. Let's see what our vibe coding assistant suggests. Before we do that though, we should save our models. We'll keep all of those and then run them. Great. We'll go ahead and run all of this code to make sure that our models are saved. You'll notice our job lib dump, though didn't quite work as we expected it to. We need to tidy up this file path and run them individually. So let's do that to get our models actually saved out. We'll do for the next one. We'll go ahead and accept this suggestion, dump that out, and then finally do the same with our XG Boost model. Now we can get to the tuning, tune, the XG Boost model. Fantastic. Let's keep this and work through it. You'll see that our assistant has…
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