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- The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts.[7][8] Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot guarantee to return the globally optimal decision tree.
- Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting.Mechanisms such as pruning are necessary to avoid this problem.
- There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems. In such cases, the decision tree becomes prohibitively large. Approaches to solve the problem involve either changing the representation of the problem domain (known as propositionalisation)or using learning algorithms based on more expressive representations (such as statistical relational learning or inductive logic programming).
- For data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels.
What else can I help you with?
Limitations of decision support system?
limitations of dss
What are the pros and cons of the classical decision making model?
One pro of the classical decision-making model is its logical and structured approach, helping to ensure thorough consideration of options. However, a con is its assumption of perfect information and rationality, which may not always reflect real-world complexities and limitations in decision-making.
What is a decision-making approach in which group decision makers are geographically dispersed throughout the country or the world?
delphi approach
What is the limitations of system approaches?
what is the limitation of social system approach
Where can you find a free program that makes it easy to make and use a decision tree?
what is a decision tree???
What is a decision theoretic approach?
A decision theoretic approach is an approach to determine how decisions are made given unknown variables and an uncertain decision environment framework. It is applied to many areas such as auctions, game theories, and marketing.
What are the limitations ofcovitional approach of managing data?
file base system
Advantages and limitations of linear programming as a managerial decision making model?
It takes out the personal angle in decision making.
Assess the contributions and limitations of economic analysis for business decision making?
There are a number of contributions and limitations which are associated with analysis for business decision making. The contributions will look at the main factors of production and the returns on investment. The limitations are seen in the assumption that such factors remain constant.?æ
What role does a decision tree play in business dicision making?
A decision tree serves as a visual and analytical tool that helps businesses map out the potential outcomes of different choices, facilitating clearer decision-making. By breaking down complex decisions into a tree-like structure of branches and nodes, it allows stakeholders to evaluate various scenarios, risks, and benefits systematically. This method enhances transparency and helps identify the most beneficial course of action based on quantitative data and qualitative factors. Ultimately, decision trees support informed decision-making by providing a structured approach to analyzing options.
What advantages does decision table have over decision tree?
could u send me the answers for the merits of the decision tables
What are the limitations of cardinal approach?
The cardinal approach, often used in decision-making and resource allocation, has several limitations. It may oversimplify complex scenarios by reducing qualitative factors to numerical values, potentially overlooking important contextual nuances. Additionally, it can lead to a focus on quantitative metrics at the expense of qualitative insights, resulting in decisions that may not align with broader goals or stakeholder values. Lastly, the approach may be vulnerable to biases in assigning and interpreting numerical values, impacting the overall reliability of the outcomes.
