Boosting and bagging are both ensemble learning techniques in machine learning that aim to improve the performance of individual models by combining the predictions of multiple base models. They work in slightly different ways and have different focuses.

Bagging (Bootstrap Aggregating): Bagging involves creating multiple base models (often the same type of model) and training them independently on different subsets of the training data. These subsets are created by randomly selecting data points from the original training set with replacement, which means that some data points may appear in multiple subsets and some may not appear at all. Each base model is then trained on its respective subset of data. Once the base models are trained, their predictions are combined through averaging (for regression) or majority voting (for classification) to make the final ensemble prediction.

Bagging helps to reduce variance and improve the stability of the model. The diversity introduced by training on different subsets of the data helps to smooth out the noise and outliers present in the data, leading to a more robust ensemble model. The most well-known algorithm that uses bagging is Random Forest.

Boosting: Boosting, on the other hand, is an iterative technique that focuses on improving the weaknesses of individual base models. In boosting, base models are trained sequentially, and at each iteration, more emphasis is given to the misclassified or poorly predicted data points from the previous iterations. The subsequent base models are trained to correct the mistakes made by earlier models.

The key idea behind boosting is to assign weights to the data points, where misclassified points are given higher weights so that the next base model focuses more on them. The final ensemble prediction is a weighted combination of the predictions from all the base models.

Boosting algorithms, such as AdaBoost (Adaptive Boosting), Gradient Boosting, and XGBoost, tend to achieve high accuracy by focusing on difficult-to-classify instances and gradually improving the ensemble’s performance.

Key Differences:

1. Training Approach: Bagging trains base models independently on random subsets of data ( also subsets of features), while boosting trains base models sequentially, giving more emphasis to misclassified instances.
2. Base Model Diversity: Bagging aims to introduce diversity by training models on different subsets of data. Boosting introduces diversity by focusing on misclassified instances and adjusting subsequent models accordingly.
3. Weighting: Boosting assigns weights to data points to emphasize misclassified instances, while bagging treats all data points equally.
4. Ensemble Prediction: In bagging, the final prediction is usually an average or majority vote of base model predictions. In boosting, predictions from all base models are combined with different weights based on their performance.
5. Performance: Boosting often achieves higher accuracy but is more prone to overfitting due to its sequential nature. Bagging focuses more on reducing variance and improving stability.
6. • Bagging helps to decrease the model’s variance.
   • Boosting helps to decrease the model’s bias.

In summary, bagging aims to create a robust ensemble model by reducing variance, while boosting focuses on improving accuracy by iteratively correcting the mistakes of previous models.

https://www.kaggle.com/code/prashant111/bagging-vs-boosting

https://www.pluralsight.com/guides/ensemble-methods:-bagging-versus-boosting

Random forest vs bagging

https://developers.google.com/machine-learning/decision-forests/random-forests

bagging:  random resample ( with same number of samples)

random forest: random sample features ( predictors, attribute sampling)

Because the decision trees of a random forest are not pruned, training a random forest does not require a validation dataset. In practice, and especially on small datasets, models should be trained on all the available data.

When training a random forest, as more decision trees are added, the error almost always decreases; that is, the quality of the model almost always improves. Yes, adding more decision trees almost always reduces the error of the random forest. In other words, adding more decision trees cannot cause the random forest to overfit. At some point, the model just stops improving. Leo Breiman famously said, “Random Forests do not overfit“.

https://developers.google.com/machine-learning/decision-forests

XGBoost vs Random Forest

XGBoost (Extreme Gradient Boosting) and Random Forests are both popular machine learning algorithms used for supervised learning tasks like classification and regression. They have some similarities but also significant differences:

1. Ensemble Methods:
   • Random Forests: Random Forest is an ensemble method based on decision trees. It builds multiple decision trees during training and combines their predictions through a majority vote (for classification) or averaging (for regression).
   • XGBoost: XGBoost is also an ensemble method, but it is based on boosting rather than bagging. It builds decision trees sequentially and adjusts their weights to correct errors made by previous trees.
2. Tree Construction:
   • Random Forests: In a Random Forest, each tree is constructed independently. The trees are typically deep and unpruned.
   • XGBoost: XGBoost, on the other hand, builds shallow trees sequentially. Each tree tries to correct the errors made by the previous ones. The trees in XGBoost are often referred to as “weak learners.”
3. Parallelism:
   • Random Forests: Random Forests can be trained in parallel because the trees are independent of each other. This makes them suitable for distributed computing and multicore processors.
   • XGBoost: XGBoost is inherently sequential since each tree depends on the previous ones. However, it offers limited parallelism at the level of building individual trees but is not as naturally parallelizable as Random Forests.
4. Regularization:
   • Random Forests: Random Forests typically rely on feature bagging (random feature subsets) and bootstrapping to reduce overfitting.
   • XGBoost: XGBoost includes a range of regularization techniques, including L1 (Lasso) and L2 (Ridge) regularization, to control model complexity and prevent overfitting. This makes XGBoost more flexible in handling overfitting.
5. Handling Missing Values:
   • Random Forests: Random Forests can handle missing values by imputing them during training.
   • XGBoost: XGBoost has built-in support for handling missing values. It automatically learns how to partition data with missing values and can make predictions even when some values are missing.
6. Performance:
   • Performance can vary depending on the dataset and the specific problem. In practice, both Random Forests and XGBoost are known for their high predictive accuracy. XGBoost often performs slightly better in many structured/tabular data problems but may require more tuning.
7. Interpretability:
   • Random Forests: Random Forests are relatively easier to interpret because they provide feature importance scores and can be visualized more intuitively.
   • XGBoost: XGBoost models are typically more complex, and interpreting feature importance can be less straightforward. However, efforts have been made to provide feature importance scores for XGBoost models as well.

In summary, both XGBoost and Random Forests are powerful ensemble methods used for various machine learning tasks. The choice between them often depends on the specific problem, the size of the dataset, and the need for interpretability. It’s common practice to experiment with both and select the one that performs better for a given task.