In this video, learn how to re-fit the model on the full training set and evaluate on the 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. …
Author
Released
5/10/2019- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Duration
Views
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Conclusion
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Next steps1m 23s
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Video: Evaluate results on validation set