Data Science Interview | CRISP-ML(Q) | Part 1 | Ensemble Techniques & Hyperparameters

Here's a summarized version in 5 bullet points:

- The topic of discussion is Ensemble techniques, which logically follows after discussing tree-based models due to the limitations of the latter.
- Barney, known for his comprehensive work in content creation and research on Ensemble techniques, is a key speaker.
- Ensemble methods encompass heterogeneous models, homogeneous models, tree and non-tree based models, generative models, and non-generative models.
- Generative models have subsequent models that are influenced or derive information from previous models, while non-generative models have each model built independently of others.
- Examples of ensemble algorithms include voting, stacking, bagging, random forest, Ada boost, gradient boosting, extreme gradient boosting, light GBM, and CAT boost.

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