Difference Between Bagging and Boosting
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Overview
In the world of machine Learning, one of the most exciting and promising concepts is Ensemble Learning. This method helps in improving machine learning results by combining several models. This contributes positively to predictive accuracy.
The primary concept behind ensemble models is to combine weak learners to form active learners. Bagging and Boosting are two types of ensemble learning.
Introduction
Ensemble learning overcomes the statistical, computational, and representational problem that arises when the hypothesis space is too large for the available data.
Briefly, bagging involves fitting many models on different samples of the dataset and averaging the predictions, whereas boosting involves adding ensemble members sequentially to correct the predictions made by prior models and outputs a weighted average of the predictions.
Bagging
Bagging stands for Bootstrap aggregating, which combines several models for better predictive results. In statistical classification and regression, bagging improves the stability and accuracy of machine learning algorithms by decreasing the variance and reducing the chances of overfitting.
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- The original dataset is divided into multiple subsets, selecting observations with replacements. This process of random sampling is called bootstrapping.
- A base model is created on each of the subsets.
- The subsets are independent of each other; hence the training of each model is done in parallel.
- We derive the final prediction by combining the predictions from all the models.
The most common implementation of bagging ensemble learning is the Random Forest .Thus, the technique makes the random selection of features rather than using all features to develop trees.
To know more about Bagging visit Bagging in Machine Learning
Boosting
Boosting involves building a strong classifier from several weak classifiers using the weak models in series. The first step is to build a model from the training set. Then we create the second model, which tries to correct the error incurred while training the first. This process is continued while adding new models until the maximum number of models is reached, or the training data is finished.
Gradient Boosting or Adaboost is an implementation of boosting. AdaBoost stands for Adaptive Boosting and is a technique that combines multiple weak classifiers into a single strong classifier. Gradient boosting uses the gradient descent algorithm that minimizes any differentiable loss function. To know more about Boosting visit Boosting in Machine Learning
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Difference Between Bagging and Boosting
| Bagging | Boosting |
|---|---|
| The original dataset is divided into multiple subsets, selecting observations with replacement. | The new subset contains the components mistrained by the previous model. |
| This method combines predictions that belong to the same type. | This method combines predictions that belong to the different types. |
| Bagging decreases variance. | Boosting decreases bias. |
| Base classifiers are trained parallelly. | Base classifiers are trained sequentially. |
| The models are created independently. | The model creation is dependent on the previous ones. |
Similarities Between Bagging and Boosting
- Both are ensemble methods that improve the stability of the machine learning model.
- Both generate one learner from multiple learners.
- The final decision is by combining the predictions of the N learners.
- Both algorithms help in dealing with the bias-variance trade-off.
- Both can be used to solve classification as well as regression problems.
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Conclusion
The key takeaways from this article are:-
- Ensemble learning helps in improving machine learning results by combining several models.
- Bagging involves fitting many decision trees on different samples of the dataset and averaging the predictions.
- Boosting involves adding ensemble members sequentially to correct the predictions made by prior models and outputs a weighted average of the predictions.