Why the bagging ensemble leads to a more sensible decision boundary?
时间: 2024-05-20 12:19:15 浏览: 11
Bagging ensemble is a technique used in machine learning to reduce the variance of a single model and improve its accuracy. It involves creating multiple models with different subsets of the training data and then combining their predictions to make a final decision.
The reason why bagging ensemble leads to a more sensible decision boundary is that it reduces the impact of outliers and noise in the training data. By training multiple models on different subsets of the data, the ensemble can identify and learn from the common patterns in the data while ignoring the noise and outliers. This results in a more stable and robust model that is less prone to overfitting and can generalize better to new data.
Furthermore, bagging ensemble can also help to reduce bias in the model by averaging out the predictions of multiple models. This can lead to a more balanced and accurate decision boundary that is less influenced by any single model's biases or limitations.
Overall, bagging ensemble can improve the performance of a machine learning model by reducing variance, reducing bias, and creating a more sensible decision boundary.
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