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While navigational freedom allowed fast learners to move through work that they knew already, weaker learners tended to get lost.
From Figure 4, the weaker learners with give much better boosting results than the stronger learners with.
Based on the characteristics of SNP genotype data, we select decision trees as weaker learners.
With increasing emphasis on active learning in neurology [ 5], our results suggest TBL may be a useful adjunct teaching method for undergraduate neurology education, particularly for academically weaker learners.
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Our combined CBoost with bagging algorithm emphasizes on weaker learner for each boosting run.
Our combined Boosting with Bagging algorithm emphasizes weaker learner for each boosting run.
A very simple supervised learning algorithm, the decision tree algorithm, is chosen as the weaker learner for our ensemble system, because it is easy to implement, can naturally capture interactions and satisfies the unstable learner requirement of an ensemble system.
Results are given using decision stumps as the weak learners.
One design decision is whether to use simple "weak learners" such as decision tree stumps or more complicated weak learners such as large decision trees or neural networks.
Another design decision is the training algorithm for the constituent weak learners.
Then, another issue to ponder is how to boost the weak learners to be strong learners.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com