Bagging and random forest are two commonly used algorithms in Machine Learning. These are Sequential and Parallel methods for training a model. Bagging reduces the number of training examples for a specific model, which leads to higher generalization ability across different unseen data sets. On the other end, Random Forest is a method that can be used to train multiple models simultaneously with little overhead.
This post covers the differences between these two Machine Learning algorithms in depth so you can choose the right one for your use case. Both algorithms have their benefits and drawbacks, but it also depends on your use case. Let’s explore them further…
What is Bagging?
Bagging is a technique used to train a model one instance at a time. The model that we have created is called a “beta” model. To create a new model, we take the beta model and “bag” it, which means we change some of the variables in our model and then create a new model with the changed variables.
When we create the new model, we then test the new model with the same training data that we used to create the beta model. The process goes on until we have created a large number of models and we then use the majority of the models to “overfit” the training data and then we use the rest of the models for “cross-validation” to get a better idea of how well our model generalizes to the test data.
What is Random Forest?
Random forest is a machine learning technique used for classification and regression. It is a supervised learning algorithm that can be used to create a model that predicts the value of a target variable by learned decision rules inferred from the data features. The algorithm works by constructing a number of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random forest is a popular method because it is accurate and scalable, meaning it can be used on large datasets. The algorithm has also been shown to be resistant to overfitting, which means it can generalize well to new data.
Difference between Bagging and Random Forest
In machine learning, there are two main types of algorithms: Bagging and Random Forest. Bagging algorithms work by creating multiple models from different subsets of the data, then averaging the predictions of those models. Random forest algorithms, on the other hand, work by creating multiple models from different subsets of the data, then selecting the model that results in the lowest error rate.
Both algorithms have their advantages and disadvantages. Bagging algorithms are generally more accurate, but they can be computationally expensive. Random Forest algorithms are less accurate, but they are much faster to train. In general, it is best to use a bagging algorithm when accuracy is more important than speed, and to use a random forest algorithm when speed is more important than accuracy.
Conclusion
The Random Forest algorithm is a powerful tool for data analysis and decision making. It has been shown to be more accurate than the bagging algorithm in many cases. However, there are some scenarios where bagging is still preferable. In general, if you have a large data set and time is of the essence, then random forest should be your go-to choice.
