Final answer:
Random Forest is a popular and effective machine learning algorithm that uses the concept of bagging. It addresses the shortcomings of single decision trees by combining the predictions of multiple trees, leading to more robust models.
Step-by-step explanation:
The machine learning algorithm that is popular and effective based on the bagging concept is Random Forest. This ensemble learning method involves creating multiple decision trees during the training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. By combining multiple decision tree-based classifiers, Random Forest mitigates some of the main issues associated with single decision trees, such as getting stuck at local extremums or being too false negative-prone. These individual trees may overfit to their respective training sets, but by averaging their results, Random Forest produces more reliable and robust predictions.
In contrast, methods like Classification and Regression Trees (CART) are known to be prone to overfitting, especially when dealing with higher-dimensional data or smaller datasets. On the other hand, Bayesian Networks (BNs) offer a more exhaustive search but might be less efficient in some cases. Random Forests, as a form of ensemble classifier, provide a balance by leveraging the strengths of multiple decision trees while correcting for their individual weaknesses.