Difference between revisions of "Random Forest in Data Analytics"
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This is a “level 3” roadmap at the technology/capability level (see Fig. 8-5), where “level 1” would indicate a market level roadmap and “level 2” would indicate a product/service level technology roadmap. | This is a “level 3” roadmap at the technology/capability level (see Fig. 8-5), where “level 1” would indicate a market level roadmap and “level 2” would indicate a product/service level technology roadmap. | ||
==Roadmap Overview== | ==Roadmap Overview== | ||
The high level workflow is depicted in the below. | |||
[[File:Section 1.JPG]] | |||
Random forest is an ensemble Machine Learning technique to boost the accuracy of prediction of future, based on data from the past. | |||
“Wisdom of crowd” | |||
Building block is decision tree; a voting scheme is used to determine the final prediction | |||
Commonly used ensemble approaches are booting, bagging, and stacking | |||
==Design Structure Matrix (DSM) Allocation== | ==Design Structure Matrix (DSM) Allocation== | ||
Revision as of 00:55, 8 October 2019
Technology Roadmap Sections and Deliverables
Unique identifier:
- 3RF-Random Forest
This is a “level 3” roadmap at the technology/capability level (see Fig. 8-5), where “level 1” would indicate a market level roadmap and “level 2” would indicate a product/service level technology roadmap.
Roadmap Overview
The high level workflow is depicted in the below.
Random forest is an ensemble Machine Learning technique to boost the accuracy of prediction of future, based on data from the past. “Wisdom of crowd” Building block is decision tree; a voting scheme is used to determine the final prediction Commonly used ensemble approaches are booting, bagging, and stacking