Questions tagged [bagging]

Question 1

The book Hands-On Machine Learning has a section on Out-of-Bag Evaluation related to Decision Trees, where it's stated that, By default a BaggingClassifier samples m training instances with ...

Question 2

Every Blog and Youtube video talks about the same steps: Choose that you have to build N number of tree and do the task 2-5 ...

Question 3

Assume that we have two separate tree regressions. I'm interested in understanding whether the product of tree regressions can be represented by a single tree. Would this be possible?

Question 4

I know when model is made to predict a float value, a common approach to report the models validation is using k-fold technique and calculating the average of all folds accuracy (here is a similar ...

Question 5

Let's say I have a clear case of overfitting where my loss curves look like this (x axis are iterations): Now I would like to try bagging to reduce the variance, where should I stop models training? ...

Question 6

I'm not completly sure about the bias/variance of boosted decision trees (LightGBM especially), thus I wonder if we generally would expect a performance boost by creating an ensemble of multiple ...

Question 7

Usually if we have $n$ observations, for each tree with form a bootstrapped subsample of size $n$ with replacement. On googling it one common explanation I've seen is that with replacement sampling is ...

Question 8

Stacking can be achieved with heterogeneous algorithms such as RF, SVM and KNN. However, can such heterogeneously be achieved in Bagging or Boosting? For example, in Boosting, instead of using RF in ...

Question 9

I found the definition: ...

Question 10

I've been doing some research on ensemble learning and read that for base models, model with high variance are often recommended (can't remember which book I read this from exactly). But, it seems ...

Question 11

I am bit confused about two concepts. From my understanding Bagging is when each data is replaced after each choice. so for example for each subset of data you pick one from population, replace it ...

Question 12

If bagging reduces overfitting than the general statement that base learners of ensemble models should have high bias and low variance(that is should be undefiting) wrong?

Question 13

The accuracy of my bagging decision tree model reach up to 97% when I set the random seed=5 but the accuracy reduce to only 92% when I set random seed=0. Can someone explain why the huge gap and ...

Question 14

I recently ran the gradient boosted tree regressor using scikit-learn via: GradientBoostingRegressor() This model depends on the following hyperparameters: Estimators ($N_1$) Min Samples Leaf ($N_2$...

Question 15

Bagging use decision tree as base classifier. I want to use bagging with decision tree(c4.5) as base as the method that improve decision tree(c4.5) in my research that solve problem overfitting. Is ...

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Questions tagged [bagging]

Average training instances sampled with bagging

How a Random forest "learns" or How loss (objective function value) is propagated back so that a random forest can "Improve"?

Can the product of tree regressions be represented by a single tree?

What are valid measures for reporting k-fold score in the case of confusion-matrix?

Where should I stop training if I want to bag models

Random LightGBM Forest

Why can't we sample without replacement for each tree in a random forest if the subsample size is large enough?

Can Boosting and Bagging be applied to heterogeneous algorithms?

Difference between bagging and pasting?

Base model in ensemble learning

bagging vs. pasting in ensemble learning

Bagging Base models

Why the accuracy of my bagging model heavily affected by random state? [closed]

Counting the number of trainable parameters in a gradient boosted tree

Can I do bagging method as improvement technique to decision tree in research?

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