Both ensemble learning techniques, boosting and bagging, aim to improve machine learning performance by combining predictions from multiple individual models. They differ from each other in many ways, such as their mechanisms, approaches and impact on model performance. We will examine the differences between boosting and bagging in this answer. Data Science Course in Pune
Bagging: Bagging, also known as bootstrap aggregation, is a technique which involves training several models independently using different subsets from the training data, and then combining them through voting or average. Bagging is a process that involves the following steps:
Bootstrap Samples: Bagging begins with a random selection of subsets from the training data using a method called bootstrap sampling. This is done by randomly selecting samples from the dataset, with replacement. Some samples may be chosen multiple times while others might not be.
Training Models: Each set of training data is used for a different model. This model is often called a weak or base learner. These models are usually trained with the same algorithm but different subsets.
Combination of Predictions: The predictions are combined after training each individual model. Combination can be done through voting (classification) or average (regression).
Bagging has many advantages.
Reduced variance: Bagging helps reduce the variance in the final model, by averaging predictions from multiple models. This reduction in variance leads to better generalization, and can even improve performance when dealing with unseen data.
Improved Stability: Bagging, which uses different subsets for each model to train the data, improves the predictability. The overall performance of the model is less affected by outliers and noise in the data.
Parallelization Bagging's independent nature allows parallel training of models. Bagging allows each model to be trained independently, making it suitable for distributed computing environments. It also reduces the training time.
Model-Agnostic: Bagging can be used with a variety of learning algorithms. It is a versatile ensemble method.
It's important to remember that bagging doesn't improve the performance of models with high bias or biased models.
The Boosting Technique: The Boosting technique is an ensemble that builds a model sequentially by concentrating on samples that are hard to classify. Unlike bagging which trains models in a linear fashion, boosting uses an adaptive approach whereby each model attempts to correct mistakes made by previous models. The main steps in boosting include: Data Science Classes in Pune
Weight Assigning: At first, all samples from the training data have equal weights. These weights are used to determine the importance of a sample in the training process.
Training Models: A first learner base is trained using the original data. The weights of each sample are adjusted to train the subsequent models. The models are then trained to pay more attention to the samples which were misclassified in the previous iteration or had higher weights.
Weight update: The weights are updated after each model has been trained so that subsequent models can pay greater attention to the samples. This adaptive weight updating process allows the model to be focused on difficult examples, and improves its performance.
Combination of Predictions: Each model's predictions are combined with a weighted vote scheme. Models that are more accurate are given a higher weighting in the final prediction.
There are many benefits to boosting.
Reduced bias: By iteratively focusing and adjusting model attention to difficult samples, Boosting can reduce bias in models. This makes boosting especially useful for models that have a high bias. Data Science Training in Pune
Accuracy Improved: By iteratively building a model, boosting can achieve a higher accuracy than individual models.
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