Ensemble learning is a machine learning technique that combines the predictions of multiple models to improve the accuracy and robustness of the predictions. The idea behind ensemble learning is that by combining multiple weak models, we can create a stronger model that can better handle complex and noisy data.
There are several different types of ensemble learning algorithms, including:
- Bagging: Bagging stands for bootstrap aggregating, which involves training multiple models on different subsets of the training data. The models are then combined by averaging their predictions or using a majority vote.
- Boosting: Boosting involves training multiple models sequentially, with each subsequent model focusing on the examples that the previous models misclassified. The final prediction is a weighted sum of the individual models.
- Stacking: Stacking involves training multiple models and then using their predictions as inputs to a meta-model, which then generates the final prediction.
- Random forests: Random forests are a type of decision tree ensemble that involve training multiple decision trees on different subsets of the data and different subsets of the features. The final prediction is made by aggregating the predictions of the individual decision trees.
Ensemble learning algorithms have been used in a variety of applications, including image and speech recognition, natural language processing, and financial forecasting. They are particularly useful in situations where the data is noisy, complex, or high-dimensional, and where the individual models may have limited predictive power on their own.