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b Difference between our predictor and other two methods.
We carried out a hold-out validation of our predictor.
Our predictor generated 1,107,876 drug-target combinations with associated leave-one-out scores.
Before we conducted our main analysis we undertook an examination of our predictor variables in respect to potential interactions between our predictor variables when modeling the probability of completion.
We train our predictor using the statistical model of PU because we are doing analysis on the offline data.
In particular, our predictor is based on the fifth algebraic order Adams-Bashforth scheme and our corrector on the sixth algebraic order Adams-Moulton scheme.
b Difference between our predictor and other two methods Fig. 6 The standard deviations of the prediction errors and the differences of different prediction methods.
This method is akin to treating institutional conditions as if they were randomly assigned across countries with respect to our predictor variables.
We relied on anonymous self-report without independent verification for both our predictor and outcome variables, but we chose this method for feasibility and to facilitate honest responses.
We have tested our predictor for the unseen data of 30,000 slot(s) and it is evaluated that there is just an error of about 0.09.
We then used a series of regressions to investigate the association between our predictor variables and acceptance of overall evolution in addition to the specific contexts of evolution.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com