Ensemble Learning
Combining multiple models to produce better predictions than any single model.
In-depth explanation
Ensemble methods leverage the wisdom of crowds—combining diverse models often outperforms individual models. Main approaches include bagging (training models on bootstrap samples, like Random Forest), boosting (sequentially training models to correct predecessors' errors, like XGBoost), and stacking (using a meta-model to combine predictions).
Examples
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
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