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LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...
A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...
Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
Thus most DM software is based on decision analysis, usually multi-criteria decision-making, and so is often referred to as "decision analysis" [5] [6] or "multi-criteria decision-making" [4] software – commonly shortened to "decision-making software". Some decision support systems include a DM software component.
Microsoft 365 is a product family of productivity software, collaboration and cloud-based services owned by Microsoft.It encompasses online services such as Outlook.com, OneDrive, Microsoft Teams, programs formerly marketed under the name Microsoft Office (including applications such as Word, Excel, PowerPoint, and Outlook on Microsoft Windows, macOS, mobile devices, and on the web), and ...
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The feature with the optimal split i.e., the highest value of information gain at a node of a decision tree is used as the feature for splitting the node. The concept of information gain function falls under the C4.5 algorithm for generating the decision trees and selecting the optimal split for a decision tree node. [1] Some of its advantages ...