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Machine Learning For Dummies
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Machine learning involves building many models and creating many different predictions, all with different expected error performances. It may surprise you to know that you can get even better results by averaging the models together. The principle is quite simple: Estimate variance is random, so by averaging many different models, you can enhance the signal (the correct prediction) and rule out the noise that will often cancel itself (opposite errors sum to zero).
Sometimes the results from an algorithm that performs well, mixed with the results from a simpler algorithm that doesn’t work as well, can create better predictions than using a single algorithm. Don’t underestimate contributions delivered from simpler models, such as linear models, when you average their results with the output from more sophisticated algorithms, such as gradient boosting.
It’s the same principle you seek when applying ensembles of learners, such as tree bagging and boosting...
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