"A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. ...
A decision tree is a flowchart-like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label (decision taken after computing all attributes). A path from root to leaf represents classification rules.
In decision analysis a decision tree and the closely related influence diagram is used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.
A decision tree consists of 3 types of nodes:
(1) Decision nodes - commonly represented by squares.
(2) Chance nodes - represented by circles.
(3) End nodes - represented by triangles.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal." [Decision tree. Wikipedia]
This marketing diagram sample was redesigned from the Wikimedia Commons file: Decision Tree on Uploading Imagesv2.svg.
[commons.wikimedia.org/ wiki/ File:Decision_ Tree_ on_ Uploading_ Imagesv2.svg]
The marketing diagram example "Decision tree" was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Marketing Diagrams solution from the Marketing area of ConceptDraw Solution Park.
www.conceptdraw.com/ solution-park/ marketing-diagrams
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. ...
A decision tree is a flowchart-like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label (decision taken after computing all attributes). A path from root to leaf represents classification rules.
In decision analysis a decision tree and the closely related influence diagram is used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.
A decision tree consists of 3 types of nodes:
(1) Decision nodes - commonly represented by squares.
(2) Chance nodes - represented by circles.
(3) End nodes - represented by triangles.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal." [Decision tree. Wikipedia]
This marketing diagram sample was redesigned from the Wikimedia Commons file: Decision Tree on Uploading Imagesv2.svg.
[commons.wikimedia.org/ wiki/ File:Decision_ Tree_ on_ Uploading_ Imagesv2.svg]
The marketing diagram example "Decision tree" was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Marketing Diagrams solution from the Marketing area of ConceptDraw Solution Park.
www.conceptdraw.com/ solution-park/ marketing-diagrams
IDEF1X Standard
Use Case Diagrams technology. IDEF1x standard - for work with relational data bases. IDEF1x standard is meant for constructing of conceptual schemes which represent the structure of data in the context of the concerned system, for example, a commercial organization."In elementary algebra, a quadratic equation (from the Latin quadratus for "square") is any equation having the form
ax^2+bx+c=0
where x represents an unknown, and a, b, and c are constants with a not equal to 0. If a = 0, then the equation is linear, not quadratic. The constants a, b, and c are called, respectively, the quadratic coefficient, the linear coefficient and the constant or free term.
Because the quadratic equation involves only one unknown, it is called "univariate". The quadratic equation only contains powers of x that are non-negative integers, and therefore it is a polynomial equation, and in particular it is a second degree polynomial equation since the greatest power is two.
Quadratic equations can be solved by a process known in American English as factoring and in other varieties of English as factorising, by completing the square, by using the quadratic formula, or by graphing." [Quadratic equation. Wikipedia]
The flowchart example "Solving quadratic equation algorithm" was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Mathematics solution from the Science and Education area of ConceptDraw Solution Park.
ax^2+bx+c=0
where x represents an unknown, and a, b, and c are constants with a not equal to 0. If a = 0, then the equation is linear, not quadratic. The constants a, b, and c are called, respectively, the quadratic coefficient, the linear coefficient and the constant or free term.
Because the quadratic equation involves only one unknown, it is called "univariate". The quadratic equation only contains powers of x that are non-negative integers, and therefore it is a polynomial equation, and in particular it is a second degree polynomial equation since the greatest power is two.
Quadratic equations can be solved by a process known in American English as factoring and in other varieties of English as factorising, by completing the square, by using the quadratic formula, or by graphing." [Quadratic equation. Wikipedia]
The flowchart example "Solving quadratic equation algorithm" was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Mathematics solution from the Science and Education area of ConceptDraw Solution Park.
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