- Predictive analytics is a type of data analysis that uses statistical, machine learning, and analytical methods to predict future outcomes.
- Ensemble learning is a machine learning technique that combines multiple models to create a more accurate prediction than any single model could achieve on its own.
- There are many different ensemble learning methods, but some of the most common include:
- Bagging: This method creates multiple models by training each model on a different bootstrap sample of the training data.
- Boosting: This method creates multiple models by training each model on the same training data, but weighting the data points differently in each iteration.
- Stacking: This method creates a meta-model that combines the predictions of multiple base models.
- Ensemble learning methods can be used to improve the predictive accuracy of a variety of machine learning models, including:
- Decision trees
- Support vector machines
- Neural networks
- Ensemble learning methods can also be used to improve the robustness of a machine learning model, making it less susceptible to overfitting or underfitting.
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Predictive Analytics with Ensemble Learning
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