Difference between revisions of "Decision Forest Regression"
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* [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] | ||
* [http://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange] | * [http://datascience.stackexchange.com/questions/9159/when-to-choose-linear-regression-or-decision-tree-or-random-forest-regression When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange] | ||
Revision as of 10:48, 12 October 2020
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- AI Solver
- Capabilities
- Regression Analysis
- Math for Intelligence
- Decision Forest Regression | Microsoft
- When to choose linear regresssion or Decision Tree or Random Forest regression? | Stack Exchange
- Data Science Concepts Explained to a Five-year-old | Megan Dibble - Toward Data Science
- 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.
