What is Bagging?
Bagging is a learning technique that helps in improving the performance, implementation, and accuracy of machine learning algorithms. Or in other words, we can say that it is basically a machine learning ensemble meta-algorithm crafted to enhance the stability and accurateness of algorithms utilised in statistical classification and regression. It is also known as Bootstrap aggregating.
What is Boosting?
Boosting is an ensemble technique that iteratively alters the weight of observation based on the last classification. It helps in enhancing the power of a machine learning program by counting more complicated or qualified algorithms. This process can also help in reducing bias and variance in machine learning.
Difference between Bagging and Boosting
|
S.NO |
Bagging |
Boosting |
|
1. |
Bagging is a learning approach that aids in enhancing the performance, execution, and precision of machine learning algorithms. |
Boosting is an approach that iteratively modifies the weight of observation based on the last classification. |
|
2. |
It is the easiest method of merging predictions that belong to the same type. |
It is a method of merging predictions that belong to different types. |
|
3. |
Here, every model has equal weight. |
Here, the weight of the models depends on their performance. |
|
4. |
In bagging, each model is assembled independently. |
In boosting, the new models are impacted by the implementation of earlier built models. |
|
5. |
It helps in solving the over-fitting issue. |
It helps in reducing the bias. |
|
6. |
In the case of bagging, if the classifier is unstable, then we apply bagging. |
In the case of boosting, If the classifier is stable, then we apply boosting. |
Keep learning and stay tuned to get the latest updates on GATE along with GATE Preparation Books & GATE Answer Key and more.
Comments