Random subspaces
Random subspaces is an ensemble technique that trains each model on a randomly selected subset of input features.
Outlier Detection in Python
Random subspace selection is a technique for constructing feature subsets by choosing dimensions randomly.
- How it is used
- Uses diverse feature subsets to support efficient and interpretable outlier detection when domain knowledge is unavailable.
- How deep it goes
- Explains selection logic, diversity, subspace size, tuning, and score comparability, but stops short of detector-specific execution.
- Examples
- Demonstrates a Python subset-selection algorithm and shows generated feature sets with limited overlap.
- What you can do after
- Build multiple balanced, diverse feature subsets for downstream detector ensembles.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Random subspaces is an ensemble sampling technique that trains each model using a randomly selected subset of features.
- How it is used
- Uses feature diversity within averaging ensembles to reduce prediction variance on tabular-data problems.
- How deep it goes
- Defines feature sampling and configuration parameters, but does not provide a dedicated worked implementation.
- Examples
- Contrasts feature-only sampling with training-example sampling and combined sampling in a strategy table.
- What you can do after
- Operate/configure BaggingClassifier or BaggingRegressor using feature subsampling and full training-example samples.
Ensemble Methods for Machine Learning
Random subspaces is an ensemble technique that trains component models using randomly selected subsets of input features.
- How it is used
- Trains parallel homogeneous ensembles efficiently when data has many features.
- How deep it goes
- Defines feature-only sampling, parameterizes it, contrasts it with related ensembles, and stops short of deriving aggregation mathematics.
- Examples
- Uses SVC, BaggingClassifier settings, and high-dimensional image or text data as concrete examples.
- What you can do after
- Apply it via scikit-learn's BaggingClassifier with feature sampling and a chosen base learner.
Read how Ensemble Methods for Machine Learning explains random subspaces →
Related concepts
- random forestcontrasts with · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
- baggingkind of · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
- feature baggingused by · Feature bagging is an ensemble technique that trains multiple detectors on randomly selected feature subsets and combines their outputs.
- subspaceproduces · A subspace is a subset of a vector space that contains the zero vector and is closed under vector addition and scalar multiplication.
- BaggingClassifierimplemented by · BaggingClassifier is a scikit-learn ensemble estimator that trains base estimators on bootstrap samples and aggregates their predictions.
- decision treeuses · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
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