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How does the effect of bagging on variance and bias?
Usually, the essence is the following: “You train a lot of Decision Trees on different parts of the training set and average their predictions into a final prediction. The prediction gets better, because the variance of the Random Forest is smaller compared to the variance of a single Decision Tree. (dartboard.png)”
Which is higher in bias, bagging or pasting?
…bagging ends up with a slightly higher bias than pasting, but… the ensemble’s variance is reduced. I am a bit confused about this part. Wouldn’t bagging have higher variance and lower bias, since the sampled instances will be more correlated with each other compared to pasting?
Is there a way to lower the bias?
Luckily, there are numerous ways to lower the bias (e.g. with a technique called Boosting) and also other ways to lower the variance. The latter can be achieved with the so-called Bagging.
What’s the effect of bagging with linear regression?
The good thing about Bagging is, that it also does not increase the bias again, which we will motivate in the following section. That is why the effect of using Bagging together with Linear Regression is low: You can not decrease the bias via Bagging, but with Boosting.
How is the variance of a random forest affected?
“You train a lot of Decision Trees on different parts of the training set and average their predictions into a final prediction. The prediction gets better, because the variance of the Random Forest is smaller compared to the variance of a single Decision Tree. (dartboard.png)” Of course, I am paraphrasing here.
Which is a variant of bagging proposed by Breiman?
Random Forests A variant of bagging proposed by Breiman It’s a general class of ensemble building methods using a decision tree as base classifier. Classifier consisting of a collection of tree-structure classifiers. Each tree grown with a random vector Vk where k = 1,…L are independent and statistically distributed.
Why do decision trees have a high variance?
This means that the Decision Tree predictions wiggle around more extremely than the Linear Regression predictions when re-sampling the training dataset, which we refer to as Decision Trees having a high variance and Linear Regression having a low variance. Summary of the algorithm properties.