What is Bagging?
Ensemble learning methods
In order to create models and enhance their functionality, machine learning employs a variety of methodologies. Methods for ensemble learning assist classification and regression models be more accurate. Several different independent models, frequently referred to as base models, are integrated to create an efficient, optimal prediction model using ensemble learning, a popular and favoured machine learning technique. An example of an ensemble learning algorithm is the Random Forest method.
Bagging: a type of ensemble learning
An ensemble learning technique called bagging, often referred to as Bootstrap aggregating, aids in enhancing the efficiency and precision of machine learning algorithms. It lowers the variance of a prediction model and is used to handle bias-variance trade-offs. Bagging, specifically decision tree methods, is used for both regression and classification models to prevent overfitting of the data.
How to perform bagging?
- Consider a training set that contains m features and n observations. You must choose a random sample without replacement from the practise dataset.
- Using sample data, a model is constructed using a subset of m features that is randomly selected.
- The nodes are divided using the feature that offers the best split among all of them.
- You have the best root nodes because the tree has matured.
- Repeating the previous steps n times. To provide the most accurate prediction, it combines the results of various decision trees.
Advantages
- Bagging minimizes the overfitting of data
- It improves the model’s accuracy
- It deals with higher dimensional data efficiently
