The two ensemble learning techniques - boosting and bagging - aim to improve the performance of machine learning by combining predictions made from different individual models. The two techniques differ in a number of ways, including their methods, impact on performance, and approaches. In this answer, we will look at the differences between bagging and boosting. Data Science Training in Pune
Bagging is also known as bootstrap aggregate. It involves the independent training of several models using different subsets of training data and then combining these through voting or average. Bagging involves the following steps.
Bootstrap samples: The bagging process begins with the random selection of subsets based on the training data, using a technique called bootstrap sampling. The samples are randomly selected from the dataset and replaced. Some samples will be selected multiple times, while others may not.
Training Models: Every set of data used in training is for a specific model. This model is also known as a base or weak learner. These models are typically trained using the same algorithm, but with different subsets.
Combination of Predictions - The predictions are combined once each model has been trained. Combination is done by voting (classification) and average (regression).
Bagging has many benefits.
Reduced variance: By combining predictions from different models, bagging helps reduce variance in the final model. This reduced variance can lead to improved generalization and even better performance when dealing unseen data.
Predictability is improved by bagging, which involves using different subsets of data for each model. Outliers and noise are less likely to affect the overall performance of the models.
Parallel Training of models is possible due to the independence of Bagging. Bagging allows for each model to train independently, making it ideal for distributed computing environments. This reduces training time.
Bagging is model-independent. It can be used in conjunction with many different learning algorithms. This is a versatile method.
Bagging does not improve the performance or accuracy of models that are biased or have high bias.
The Boosting Technique - The Boosting Technique is a group that builds a sequential model by focusing on samples that are difficult to classify. The boosting technique is an adaptive method that uses a model to try and correct errors made by the previous model. The steps involved in boosting are: Data Sciences Classes in Pune
Weight Assigning : All samples from the data of training are assigned equal weights at first. These weights determine the importance of each sample during the training process.
The original data is used to train a first learner base. Weights are adjusted for each sample to train subsequent models. The next step is to train the models to pay attention to samples that were misclassified or had a higher weight in the previous model.
Weight update: After each model is trained, the weights are updated so that future models can focus more on the samples. This adaptive weight update process improves the performance of the model by allowing it to focus on difficult examples.
Combination of Predictions - Each model's prediction is combined using a weighted voting scheme. The models that are more accurate receive a greater weighting for the final prediction.
Boosting has many advantages.
Reduced bias. By iteratively adjusting and focusing model attention on difficult samples, Boosting reduces bias in models. This makes boosting particularly useful for models with a high level of bias. Data Science Classes in Pune
Accuracy Increased: By building models iteratively, boosting is able to achieve higher accuracy than single models.
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