Random patches
Random patches is an ensemble technique that trains models on randomly selected subsets of both observations and features.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Random patches is an ensemble sampling technique that trains models on randomly selected subsets of both observations and features.
- How it is used
- Reduces variance by averaging models trained on varied subsets of tabular observations and features.
- How deep it goes
- Defines the sampling contrast among four averaging strategies and connects diversity to more reliable predictions.
- Examples
- Uses a comparison table and random-forest diagrams showing varied data partitions, tree structures, and majority voting.
- What you can do after
- Evaluate ensemble sampling choices for reducing variance and producing more reliable predictions.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains random patches →
Ensemble Methods for Machine Learning
Random patches is an ensemble technique that trains models using independently sampled subsets of both training examples and input features.
- How it is used
- Trains diverse parallel ensembles for high-dimensional data by varying both the examples and features supplied to each learner.
- How deep it goes
- Explains row-and-column sampling, estimator choice, implementation parameters, and contrast with random forests.
- Examples
- Uses image and text databases, SVC learners, and BaggingClassifier configurations with row and feature sampling.
- What you can do after
- Apply random patches through BaggingClassifier or another base learner to increase diversity and smooth predictions.
Read how Ensemble Methods for Machine Learning explains random patches →
Related concepts
- baggingkind of · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
- random subspacesuses · Random subspaces is an ensemble technique that trains each model on a randomly selected subset of input features.
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