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 ...
Sivaram Rasathurai's user avatar
2 votes
Accepted

What ensemble methods are used in the state-of-the-art models?

In my opinion, it is not because ensemble methods are not good, just the state-of-the-art and Kaggle competitions are two different fields. Kaggle competitions can be understood as an industry project ...
CuCaRot's user avatar
  • 912
1 vote

Can i train xgboost on multiple time series csv files at the same time?

The short answer is no. Time series models model a single time series. If you want to model N time series you need N time series models. XGBoost is no different when used for time series modeling. The ...
Brian O'Donnell's user avatar
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 ...
Saurav Maheshkar's user avatar
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 ...
Robby Goetschalckx's user avatar
1 vote

Why would the application of boosting prevent underfitting?

It seems to me that the article is approaching it from the perspective of the base classifier. For example if the base classifier is a Decision Tree with a max depth of 1 (or any other severely ...
A_Arnold's user avatar
  • 239

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