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
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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