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Related Concept Videos

Variation01:19

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Survival Tree01:19

Survival Tree

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 survival tree begins...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

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Related Experiment Videos

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

Kristin K Nicodemus1, James D Malley, Carolin Strobl

  • 1Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. kristin.nicodemus@well.ox.ac.uk

BMC Bioinformatics
|March 2, 2010
PubMed
Summary
This summary is machine-generated.

Random forests (RF) variable importance measures (VIMs) show increased importance for correlated predictors when they are associated with outcomes. Unconditional VIMs are unbiased under the null hypothesis, offering a practical choice for large datasets.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Machine Learning

Background:

  • Random forests (RF) are widely applied in genetic association and microarray studies.
  • High predictor correlation is common in these applications.
  • Conflicting conclusions exist regarding RF variable importance measures (VIMs).

Purpose of the Study:

  • To synthesize contradictory findings on RF VIMs.
  • To evaluate RF VIM performance under predictor correlation.
  • To clarify the behavior of different VIM types.

Main Methods:

  • Extended simulation study.
  • Analysis of permutation-based VIMs in RF.
  • Comparison of unconditional, conditional, and scaled VIMs.

Main Results:

  • Unconditional RF VIMs favor correlated predictors when associated with outcomes (HA), but are unbiased under the null hypothesis (H0).
  • Conditional VIMs reduce importance for correlated predictors under HA and are unbiased under H0.
  • Scaled VIMs demonstrate bias under both HA and H0.

Conclusions:

  • Unconditional unscaled VIMs are computationally efficient and unbiased under H0.
  • The interpretation of increased VIMs for correlated predictors depends on the application.
  • Correlated predictors may be advantageous in genetic studies but can lead to spurious signals.