Search results
Results from the WOW.Com Content Network
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 ...
Performing a probabilistic risk assessment starts with a set of initiating events that change the state or configuration of the system. [3] An initiating event is an event that starts a reaction, such as the way a spark (initiating event) can start a fire that could lead to other events (intermediate events) such as a tree burning down, and then finally an outcome, for example, the burnt tree ...
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.
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.
In 2018, Google's processes and training programs in applied data science were renamed to "decision intelligence" [5] to indicate the central role of actions and decisions in the application of data science. The extent to which the theoretical frameworks drew on the managerial and social sciences in addition to data science was an additional ...
The Classification Tree Method is a method for test design, [1] as it is used in different areas of software development. [2] It was developed by Grimm and Grochtmann in 1993. [3] Classification Trees in terms of the Classification Tree Method must not be confused with decision trees. The classification tree method consists of two major steps ...
In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm , and is typically used in the machine learning and natural language processing domains.
A fast-and-frugal tree is a classification or a decision tree that has m+1 exits, with one exit for each of the first m −1 cues and two exits for the last cue. Mathematically, fast-and-frugal trees can be viewed as lexicographic heuristics or as linear classification models with non-compensatory weights and a threshold.