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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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A complete procedure for testing a claim about a population proportion is provided here.
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Hardy-Weinberg Principle01:49

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Binomial Probability Distribution01:15

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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Conditional permutation importance revisited.

Dries Debeer1,2,3, Carolin Strobl4

  • 1University of Zurich, Psychological Methods, Evaluation and Statistics, Binzmuehlestrasse 14, Box 27, Zurich, 8050, Switzerland. dries.debeer@kuleuven.be.

BMC Bioinformatics
|July 16, 2020
PubMed
Summary
This summary is machine-generated.

Improved Conditional Permutation Importance (CPI) methods enhance random forest interpretability and stability. These updates make variable importance calculations faster and more practical for researchers using R.

Keywords:
Conditional permutation importanceRRandom forest

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Area of Science:

  • Computational statistics
  • Machine learning

Background:

  • Random forest models are widely used for variable importance assessment.
  • Conditional Permutation Importance (CPI) is a popular but improvable measure.
  • Existing CPI methods offer partial variable importance quantification.

Purpose of the Study:

  • To improve the methodology and implementation of Conditional Permutation Importance (CPI).
  • To enhance the practical value and interpretability of random forest variable importance.
  • To introduce a threshold parameter for adjusting CPI's partiality.

Main Methods:

  • Extensive simulations were conducted to evaluate proposed CPI improvements.
  • Methodological enhancements focused on increasing interpretability and stability.
  • Implementation improvements aimed to reduce computation time and increase applicability.

Main Results:

  • Improved CPI methodology demonstrated increased interpretability and computational stability.
  • The new implementation significantly decreased computation times.
  • The enhanced CPI is more widely applicable and available as an R package.

Conclusions:

  • The revised CPI methodology and implementation are computationally efficient and yield stable results.
  • These improvements enhance the interpretability of random forest analyses in practical research.
  • The updated CPI offers a more robust tool for variable importance assessment.