What is Ensemble learning?
Ensemble learning, a machine learning technique, combines multiple models (like regression models or neural networks) to improve prediction accuracy. This approach tackles limitations of small datasets by leveraging the strengths of various models, ultimately creating a more accurate overall model than any single model could be on its own.
Different kinds of ensemble models
Machine learning ensemble learning techniques are generally divided into two categories in the literature: sequential and parallel.
– Each base learner is trained independently of the others using parallel approaches. Therefore, true to its name, parallel ensembles instruct base learners independently and in parallel.
– Sequential approaches minimize errors from the prior model trained in the previous step by training a new base learner in that order. Stated differently, sequential approaches build basic models step-by-step.9.
Why ought we to think about employing an ensemble?
There are two primary, linked reasons to choose an ensemble over a single model:
- Performance: Compared to a single contributing model, an ensemble can perform better and produce more accurate predictions.
- Robustness: The spread or dispersion of the model’s performance and predictions is lessened by an ensemble.
- When solving a predictive modeling challenge, ensembles outperform individual predictive models in terms of prediction accuracy. This is accomplished by the model adding bias in order to reduce the variance component of the prediction error.
FAQ’s
What is ensemble learning in machine learning?
Ensemble learning is a technique in machine learning that combines multiple models, such as regression models or neural networks, to improve prediction accuracy. By leveraging the strengths of various models, ensemble learning creates a more accurate overall model than any single model could achieve on its own.
What are the different types of ensemble learning approaches?
Ensemble learning techniques are generally divided into two categories: sequential and parallel. In parallel approaches, each base learner is trained independently and simultaneously. In contrast, sequential approaches train a new base learner in a step-by-step manner to minimize errors from the prior model.
Why is ensemble learning often preferred over using a single model?
Ensemble learning is preferred because it enhances performance and robustness. An ensemble typically yields more accurate predictions than a single model and reduces the spread or dispersion of the model’s performance and predictions, making the results more reliable.
How does ensemble learning improve prediction accuracy?
Ensemble learning improves prediction accuracy by combining the biases and variances of multiple models. This process adds bias to reduce the variance component of the prediction error, leading to more accurate and stable predictions compared to individual models.
