Sentence examples for prior data transformation from inspiring English sources

There is no need for prior data transformation or elimination of outliers.

Advantages of BRT models include superior predictive performance compared to most traditional modelling methods, ability to handle different types of explanatory variables (data can be categorical, numeric or binary), ability to accommodate missing data, and they do not require elimination of outliers or prior data transformation [ 10].

Prior to data transformation the lowest coefficients of variations for single gene expression were 45.3% and 24.5% for colon and prostate datasets respectively.

Because of the non-normality of length of stay, formal trimming [ 76, 77] or truncation [ 71, 77, 78] of the data, or deleting [ 5, 73, 79] "outliers" has been undertaken prior to possible data transformation.

This difference could be related to that fact that data for some of the variables were transformed prior to hierarchical partitioning analyses, while boosted regression trees analyses do not require data transformation prior to analysis.

The type of the regression obtained in SLP training should be controlled by the sort of cost function as well as by training parameters (the number of iterations, learning step, etc).. Whitening data transformation prior to training the perceptron is a tool to incorporate a prior information into the prediction rule design, and helps both to diminish the generalization error and the training time.

An analytics job was modified to include the data transformation prior to the computation.

It was found that the use of log counts when normalized for sequence depth is a good strategy for data transformation prior pathway analysis.

For accurate molecular subtyping, we present an alternative approach to gene centering that incorporates a routine data transformation step prior to subtyping.

No data transformation or standardization was done prior to analysis.

The use of the g-prior reduces the number of false positives, while the data transformation substantially reduces the size of the inferred network.