Difference between revisions of "Decision Forest Regression"
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** [[...predict values]] | ** [[...predict values]] | ||
* [[Capabilities]] | * [[Capabilities]] | ||
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* [[Statistics for Intelligence]] | * [[Statistics for Intelligence]] | ||
* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft] | * [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft] | ||
Revision as of 02:20, 13 July 2019
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- Capabilities
- Regression Analysis
- Statistics for Intelligence
- Decision Forest Regression | Microsoft
- When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange
- Feature Exploration/Learning
Decision trees are non-parametric models that perform a sequence of simple tests for each instance, traversing a binary tree data structure until a leaf node (decision) is reached. Decision trees have these advantages:
- They are efficient in both computation and memory usage during training and prediction.
- They can represent non-linear decision boundaries.
- They perform integrated feature selection and classification and are resilient in the presence of noisy features.
This regression model consists of an ensemble of decision trees. Each tree in a regression decision forest outputs a Gaussian distribution as a prediction. An aggregation is performed over the ensemble of trees to find a Gaussian distribution closest to the combined distribution for all trees in the model.
