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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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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
Constructing a...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

10.4K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

3.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Related Experiment Video

Updated: Oct 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Decision Rules Derived from Optimal Decision Trees with Hypotheses.

Mohammad Azad1, Igor Chikalov2, Shahid Hussain3

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

This study introduces decision trees with hypotheses, enhancing information representation. Decision rules from these trees often outperform those from conventional decision trees, offering better data analysis tools.

Keywords:
decision ruledecision treehypothesisrepresentation of information

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Conventional decision trees rely on single-attribute queries.
  • This research explores decision trees incorporating hypothesis-based queries, akin to equivalence queries in exact learning.

Purpose of the Study:

  • To develop and evaluate decision trees with hypotheses for optimal information representation.
  • To compare decision rules derived from hypothesis-based trees against conventional ones.

Main Methods:

  • Modified dynamic programming algorithms for optimal decision tree construction (minimum depth or internal nodes).
  • Experimental comparison of decision rules from hypothesis-based and conventional decision trees.
  • Utilized UCI Machine Learning Repository datasets and randomly generated Boolean functions.

Main Results:

  • Decision trees with hypotheses can be optimized for depth or internal node count.
  • Decision rules from hypothesis-based trees demonstrated superior length and coverage in many cases.
  • Empirical evidence supports the effectiveness of hypothesis-based decision trees.

Conclusions:

  • Decision trees with hypotheses offer a more effective method for information representation.
  • Hypothesis-based decision rules provide advantages over traditional decision rules in data analysis.
  • Further research into hypothesis-based decision trees is warranted for advanced machine learning applications.