The term “bagging,” which is an abbreviation for “Bootstrap Aggregating,” refers to a method used in machine learning to improve model accuracy and stability by averaging the output of several models. Each model in the ensemble is trained independently utilizing a subset of the training examples drawn at random using a replacement strategy.
Applications of Bagging
Bagging improves a model’s generalization performance by decreasing its variance through training multiple models on separate subsets of the data. This is especially helpful for algorithms with significant variances, like decision trees, which often overfit the data. The bagging technique can benefit several ML algorithms, including DT, RF, and SVM.
In artificial intelligence, bagging is a technique widely used to strengthen the reliability of machine learning models. Examples of bagging’s use in artificial intelligence include the following:
1. Image and speech recognition
Bagging can be used to enhance the performance of models for image and speech recognition. For better results in image recognition, it is possible to train multiple models independently on separate parts of the training data and then combine their predictions. Similarly, using bagging, several speech recognition models can be trained independently using different sets of audio data. The resulting predictions can then be combined to enhance the accuracy of the model.
2. Credit risk assessment
Bagging has been widely adopted as a method for improving the accuracy of models used in credit risk assessment—determining the probability that a borrower will not repay a loan. Bagging can help in reducing the variance of a model by training multiple models on different subsets of the data, ultimately improving the overall accuracy.
3. Fraud detection
Bagging can increase the precision of models for detecting financial transaction fraud. Bagging is a technique for improving model accuracy and reducing false positives and negatives by training multiple models on different subsets of the data.
4. Ensemble learning
Combining the predictions of multiple models to improve overall performance is known as ensemble learning, and bagging is frequently used as a component of this technique. When combined with other models, such as boosting and stacking, the predictions of multiple models trained with different subsets of data can significantly increase accuracy.
5. Random forests
A standard machine learning algorithm, random forests, employs bagging to increase the precision of decision trees. Random forests can reduce overfitting and improve model accuracy by training multiple decision trees on separate subsets of the training data and averaging their predictions.
6. Natural language processing
Bagging has applications in natural language processing, specifically text classification, sentiment analysis, and named entity recognition. Bagging can increase the NLP model’s accuracy and decrease the variance of the predictions by training multiple models on different subsets of the data and combining their predictions.
7. Time series forecasting
Bagging can enhance the precision of those models that extrapolate future values from existing data. By combining the predictions of multiple models trained on a different subset of the data, bagging can boost forecast accuracy and decrease prediction variance.
8. Customer segmentation
The accuracy of models that group customers based on their characteristics and behaviors can be improved with the help of bagging, which can be used in customer segmentation tasks. Bagging helps improve the precision of segmentation and gain a more insightful understanding of consumer preferences and behavior by training multiple models on different subsets of data and combining their predictions.
9. Anomaly detection
Bagging can help improve outlier detection accuracy in anomaly detection tasks. Bagging boosts the efficacy of an outlier detection algorithm and decreases the false positives it produces by training multiple models on separate subsets of the data and then combining their predictions.
