Questions tagged [ensemble-modeling]

In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.

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1answer
25k views

Adaboost vs Gradient Boosting

How is AdaBoost different from a Gradient Boosting algorithm since both of them use a Boosting technique? I could not figure out actual difference between these both algorithms from a theory point of ...
59
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5answers
84k views

GBM vs XGBOOST? Key differences?

I am trying to understand the key differences between GBM and XGBOOST. I tried to google it, but could not find any good answers explaining the differences between the two algorithms and why xgboost ...
1
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1answer
23 views

What is the form of data used for prediction with generalized stacking ensemble?

I am very confused as to how training data is split and on what data level 0 predictions are made when using generalized stacking. This question is similar to mine, but the answer is not sufficiently ...
1
vote
1answer
45 views

How does bagging help reduce the variance

I learned that bagging helps reduce variance by averaging but I couldn't understand this. Can someone explain this intuitively?
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3answers
116 views

Ensembling expressions

I have two models, $m_1$ and $m_2$, and I want to ensemble them into a final model. I want to be able weight one or the other more according to a grid search. There are two main ideas that come to my ...
6
votes
1answer
218 views

How predictions of level 1 models become training set of a new model in stacked generalization.

In stacked generalization, if I understood well, we divide the training set into train/test set. We use train set to train M models, and make predictions on test set. Then we use the predictions as ...