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karthik3k
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Bagging ?
What do you mean by Bagging in Mathematics ?
What do you mean by Bagging in Mathematics ?
Bagging, short for Bootstrap Aggregation, is a technique used in statistics and machine learning to improve the accuracy and stability of predictive models. It involves creating multiple models using subsets of the original data and then combining their predictions to make a final prediction.
Bagging works by taking a random sample with replacement from the original dataset and using it to train a model. This process is repeated multiple times, with each iteration using a different random sample. The final prediction is made by aggregating the predictions from all the models created.
The main benefit of bagging is that it reduces the variance of a model, making it more accurate and robust. It also helps to prevent overfitting by using different subsets of data for each model, and it can be applied to a wide range of predictive models.
Yes, bagging can be used for any type of data, including numerical, categorical, and text data. However, it is most commonly used for classification and regression problems.
One potential limitation of bagging is that it may not improve the performance of a model if the dataset is already small or if the underlying data is highly correlated. In addition, bagging may be computationally expensive due to the creation of multiple models.