Downsides?It’s excellent for implementation because everything runs in parallel.
Building, training, and deploying can run in different CPUs, so it’s quite easy to model these types of functions.
It’s also easy to run several trees at the same time to compare the best features of your trees.
Issues with the tree is that even though there are many sets, the models will always remain somewhat correlated.
They’re harder to interpret than a single tree because you’re combining several, and the hierarchy may not be as apparent.
The presence of outliers could be a good or a bad thing depending on your data.
Only some of your trees will see the outlier, so they’re implicitly hidden.
In some cases, outliers could have too much of an impact on the model, so it could bring your information back into what really represents your data.
On the other hand, sometimes outliers offer valuable insights into your data, and you may miss them altogether.
Bagging And BoostingLemagnen goes through the steps for both types of ensemble methods, bagging and boosting.
For both processes, it shows insights through the data, bringing up training methods and compensating for errors in each previous decision tree.
Watch his methods through Github during his talk to find out how each technique gets you closer to the truth and how these two processes enhance each weak learner to reveal those essential data insights.
Ensembling Isn’t MagicIf your ensemble uses bad models, you aren’t going to reveal insights in the data magically.
However, if you’ve got a few different weak learners that don’t offer much in practice by themselves, ensembling could build on each strength.
For the full steps, be sure to watch Lemagnen as he moves through each method of ensembling.
There are different reasons to bag or boost and a variety of programs that can help you move through these processes and take full advantage of what ensembling can do for your predictive data collection.
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