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29 the fundamental difference between bagging and random forest is that in random forests, only a subset of features are selected at random out of the total and the best. If a random forest is built using all the predictors, then it is equal to bagging. Can you give me an example for each?
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Bagging is a technique to reduce the variance of an predictor/estimator/learning algorithm. What's the similarities and differences between these 3 methods: Bagging draws a bootstrap sample of the data (randomly select a new sample with replacement from the existing data), and the results of these random samples are aggregated.
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Let's say we want to build random forest.
Wikipedia says that we use random sample with replacement to do bagging. The main difference between bagging and random forests is the choice of predictor subset size. I don't understand why we can't use random sample. If we compare it to a bagging algorithm, then it does actually have less bias than the bagging algorithm since now we're incorporating the whole dataset and we're also focusing.
Which is the best one? Is there a difference between using a bagging classifier with base_estimaton=decisiontreeclassifier and using just the randomforestclassifier? With bootstrapping and bagging, we resample from the dataset and end up building a model or estimating some sample statistic using the sampled data, which typically consists. However, i have never seen a formal mathematical proof of this statement.
