Bagging is the term from "Bootstrap Aggregation Algorithm", That is for Low Bias & Low Variance
The statistical method known as Bootstrap Aggregating, or "bagging," is a type of ensemble techniques. ensemble learning techniques are commonly used to reduce variance within a noisy dataset. This approach is used to improve classification by combining classifications of randomly generated dataset.
Bootstrap Aggregating was proposed by Leo Breiman in 1994, who was statistician and had done many research on statistics. It was derived from the concept of bootstrapping which was developed by Bradley Efron
=>Input the Dataset D.
=>Let k be the number of bootstrap samples.
=>for i = 1 to k do
=>Create a bootstrap sample of size n , ๐ท_๐ (n < D).
=>Train the Base Classifier ๐ถ_๐ on the bootstrap sample ๐ท_๐.
=>end for
=>๐ถ^โ(x) = argmax ฮฃ_๐ ๐ฟ(๐ถ_๐(x = y)). ๏ฟฝ{๐ฟ(.) = 1, if its argument is True and 0 otherwise}