What is ensemble learning technique in machine learning?
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Ensemble learning in machine learning is a technique that combines multiple models to improve overall performance and accuracy. It leverages the strengths of various models by aggregating their predictions, often resulting in better generalization and robustness compared to individual models. Common ensemble methods include bagging, boosting, and stacking.
Ensemble learning in machine learning is a technique that combines multiple models to improve overall performance and accuracy. It leverages the strengths of various models by aggregating their predictions, often resulting in better generalization and robustness compared to individual models. Common ensemble methods include bagging, boosting, and stacking.
A method of machine learning called ensemble learning combines multiple models to produce predictive performance that is superior to that of any individual model. The thought is to coordinate different models to make a more exact, vigorous, and summed up arrangement. Group techniques can be especially viable in decreasing overfitting and working on model steadiness.
There are a few different kinds of ensemble learning methods:
1. ** Bundling (or Bootstrap Aggregating)**: This includes preparing numerous forms of similar calculation on various subsets of the preparation information, commonly made by bootstrapping (irregular testing with substitution). The individual models’ predictions are then averaged (in the case of regression) or voted on (in the case of classification). A well-known bagging algorithm that combines multiple decision trees is Random Forest.
2. ** Boosting**: In boosting, models are trained in order, with each model trying to fix what went wrong with the previous model. The final prediction is the weighted sum of all models’ predictions. Techniques for boosting include algorithms like AdaBoost, Gradient Boosting, and XGBoost.
3. ** Stacking, or the Stacked Generalization**: Stacking involves training multiple models (level-0 models) and then combining their predictions with those of another model (level-1 models or meta-learners). The meta-learner tries to figure out the best way to combine the outputs of the base models.
4. ** Voting**: For classification or regression, this is a straightforward ensemble method in which the predictions of various models are combined through majority voting or averaging. There are two different ways to vote: soft voting, in which the average of the predicted probabilities serves as the basis for the final prediction, and hard voting, in which the mode of the predicted class labels serves as the basis for the final prediction.
The strength of outfit learning lies in its capacity to use the qualities and relieve the shortcomings of individual models, prompting worked on generally execution.