6 votes
Accepted

How do weak learners become strong in boosting?

As @desertnaut mentioned in the comment No weak learner becomes strong; it is the ensemble of the weak learners that turns out to be strong Boosting is an ensemble method that integrates multiple ...
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  • 266
2 votes

Has "deep vs. wide" been resolved?

I am not sure what you are really looking for but I leave here this paper here, where some intuition into that direction is provided. This paper compares the performance of a deep learning model ...
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  • 998
2 votes
Accepted

Why is the exponential loss used in this case?

The loss is $$\mathcal{L}=\sum_{i=1}^{N} \ell\left(y_{i}, f\left(\mathbf{x}_{i}\right)\right) \equiv \sum_{i=1}^{N} \exp \left(-y_{i} f\left(\mathbf{x}_{i}\right)\right),$$ which can also be written ...
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  • 33k
1 vote
Accepted

House price inflation modelling

The sold date is a feature like any other. You can do this as follow. I am assuming the features are in a pandas data frame called df where the column date is called date. Easiest way is to use the ...
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  • 614
1 vote

How do weak learners become strong in boosting?

In Boosting, we improve the overall metrics of the model by sequentially building weak models and then building upon the weak metrics of previous models. We start out by applying basic non-specific ...
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1 vote

How do weak learners become strong in boosting?

You take a bunch of weak learners, each of them trained on a subset of the data. You then just get all of them to make a prediction, and you learn how much you can trust each one, resulting in a ...
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1 vote

What are some applications where tree models perform better than neural networks?

Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see here and here), I would say tree-based models e.g. Random Forests, Decision Trees, ...
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  • 600

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