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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Non-equilibrium in the Cell01:16

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Decision trees: from efficient prediction to responsible AI.

Hendrik Blockeel1,2, Laurens Devos1,2, Benoît Frénay3

  • 1Department of Computer Science, KU Leuven, Leuven, Belgium.

Frontiers in Artificial Intelligence
|August 11, 2023
PubMed
Summary
This summary is machine-generated.

Decision trees remain highly relevant in machine learning and artificial intelligence. This review covers their evolution, strengths, and weaknesses in data science applications.

Keywords:
combinatorial optimizationdecision treesensemblesexplainable AIlearning under constraintsmachine learningresponsible AI

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Decision trees have a long history in computational intelligence.
  • Understanding their development is crucial for modern AI applications.

Purpose of the Study:

  • To provide a comprehensive overview of decision tree evolution.
  • To analyze the strengths and weaknesses of decision trees.
  • To highlight the enduring practical and theoretical relevance of decision trees in AI.

Main Methods:

  • Historical review of decision tree research.
  • Contextual analysis of decision tree applications.
  • Synthesis of strengths and weaknesses.

Main Results:

  • Decision trees have evolved significantly over four decades.
  • Key strengths include interpretability and ease of use.
  • Weaknesses involve potential for overfitting and sensitivity to data.

Conclusions:

  • Decision trees continue to be a vital tool in machine learning.
  • Their theoretical underpinnings and practical applications remain significant for AI.
  • Further research can enhance their capabilities and address limitations.