– What is Bootstrapping?
– What is overfitting?
– What is the difference between Decision Trees, Bagging and Random Forest?
Bootstrap aggregation, or bagging, is a popular ensemble learning technique used in machine learning to improve the accuracy and stability of classification and regression models. The basic idea behind bagging is to train multiple models using random subsets of the training data and then combine their predictions to reduce variance and improve the overall accuracy of the model.
Here’s how bagging works:
- Data Preparation: The first step is to prepare the training data. The training data is created using bootstrapping. Given the training data, N more training sets are created using random samples drawn from the original dataset with replacement.
- Model Training: Once the data is prepared, N base models are trained independently. Each model can use any learning algorithm such as decision trees, neural networks, support vector machines, or any other algorithm.
- Prediction: After the N models are trained, they are used to make predictions on the test dataset. The final prediction is obtained by aggregating the predictions of all N models.
- Aggregation: Aggregation can be done using different methods depending on whether it is a classification or regression problem. In classification problems, the most common aggregation method is to take the majority vote of the N models. In regression problems, the most common aggregation method is to take the average of the predictions of the N models.
Advantages of Bagging
Bagging has several advantages over traditional single model methods. It reduces the risk of overfitting by reducing the variance in the model’s predictions, and it also reduces the bias by averaging multiple models’ predictions. Bagging is particularly useful when the underlying model is unstable or prone to overfitting. One of the most popular examples of bagging is the Random Forest algorithm, which is an ensemble of decision trees trained using bagging.