Difference between revisions of "Decision Forest Regression"
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| − | [ | + | [https://www.youtube.com/results?search_query=Decision+Forest+Regression YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Decision+Forest+Regression+machine+learning+ML+artificial+intelligence ...Google search] |
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] |
** [[...predict values]] | ** [[...predict values]] | ||
| − | * [[ | + | * [[Regression]] Analysis |
| − | * [ | + | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] |
| + | * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/decision-forest-regression Decision Forest Regression | Microsoft] | ||
| + | * [https://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] | ||
| + | * [https://towardsdatascience.com/data-science-concepts-explained-to-a-five-year-old-ad440c7b3cbd 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: | 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 are efficient in both computation and [[memory]] usage during training and prediction. |
* They can represent non-linear decision boundaries. | * They can represent non-linear decision boundaries. | ||
* They perform integrated feature selection and classification and are resilient in the presence of noisy features. | * They perform integrated feature selection and classification and are resilient in the presence of noisy features. | ||
Latest revision as of 22:50, 5 March 2024
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Regression Analysis
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- 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.
