Summary
In this chapter, we discussed how ensembling multiple solutions works and proposed some basic code examples you can use to start building your own solutions. We started from the ideas that power model ensembles such as random forests and gradient boosting. Then, we moved on to explore the different ensembling approaches, from the simple averaging of test submissions to meta-modeling across multiple layers of stacked models.
As we discussed at the end, ensembling is more an art form based on some shared common practices. When we explored a successful complex stacking regime that won a Kaggle competition, we were amazed by how the combinations were tailored to the data and the problem itself. You cannot just take a stacking, replicate it on another problem, and hope that it will be the best solution. You can only follow guidelines and find the best solution consisting of averaging/stacking/blending of diverse models yourself, through lots of experimentation and computational...
