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Free sign up"unbalanced data" is a correct and commonly used phrase in written English, especially in the context of statistics and data analysis.
You can use "unbalanced data" to describe a dataset that has an unequal distribution or representation of its variables. For example: "The results of our study were inconclusive due to the unbalanced data set - there were significantly more male participants than female." "Using machine learning algorithms on unbalanced data can result in biased predictions." "In order to achieve more accurate results, we need to address the issue of unbalanced data in our sampling process."
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Furthermore, HLM can accommodate unbalanced data, thus eliminating the requirement to have the same number of individuals within each cluster.
Different from the standard metrics, the proposed method is able to handle unbalanced data and takes into account the size of the available data.
Multilevel models can accommodate unbalanced data due to attrition or missing values.
A mixed model was used to handle unbalanced data with correlated outcomes and missing data.
Hierarchical models accommodate unbalanced data and take into account the nested relationships among data points, thereby preventing pseudoreplication.
It is developed to manage highly unbalanced data, to autoparameterize itself under low computational cost, and to improve results against brute-force search.
The propensity score method introduced by Rosenbaum and Rubin is an effective tool to reduce bias in nonrandomized studies including unbalanced data [ 10].
HRQOL trajectories and patient characteristics associated with HRQOL were investigated using mixed linear models due to their ability to handle unbalanced data.
Furthermore, the methods suggest by the author had less concentration on overfitting, being able to handle unbalanced data and discover more stable models.
Moreover, by using an appropriate covariance structure, the model allows us to have unbalanced data with different number of observations per patient and different timing of these.
The linear mixed model handles unbalanced data, enabling all available information from randomised patients to be included, as well as from dropouts.
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