Final answer:
Ensemble methods in model building combine multiple individual models into a single predictive model to improve accuracy and reduce overfitting. Techniques such as bagging and boosting are commonly used for this purpose. Random forest is one example of an ensemble method that combines decision trees.
Step-by-step explanation:
Ensemble methods in model building are techniques that combine multiple individual models into a single predictive model. These methods aim to improve prediction accuracy and reduce overfitting by leveraging the strengths of different models. One popular ensemble method is called bagging, where multiple models are trained on different subsets of the training data and their predictions are aggregated. Another ensemble method is boosting, which iteratively trains weak models that focus on the data points that the previous models struggled with.
For example, in the random forest algorithm, which is an ensemble method based on bagging, a set of decision trees are trained on random subsets of the training data. Each tree then independently predicts the target variable, and the final prediction is made by aggregating the individual predictions. This helps to reduce the impact of individual decision trees that may have made errors or overfit the data.
Ensemble methods are widely used in various domains, including machine learning, data mining, and predictive analytics. They have been shown to produce more accurate and robust predictions compared to individual models, especially in cases where the underlying data is complex or noisy.