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The validation of our model using training and validation cohorts, as well as in the different time frames, supports its generalizability.
Lee compared ANN model and decision tree model using training and test datasets generated from two groups of patients with different payment schemes, i.e. payment by patients themselves and by public insurance.
An evaluation of the predictive ability of the same model using training (70%) and testing (30%) data sets yielded a weak relationship (R2 = 0.16) across the entire land area of the five ranches.
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The model uses training data for classification by estimating uncertain quantities using the Bayes theorem.
Following construction of PLS-DA models using training sets, the models were then used to predict class membership of the subjects in the test sets.
Our studies show that the method of gene selection is strictly paired with learning classifier, which determines the final predictive model by using training data.
Like others, we developed and validated our model by using training and a validation group [ 22, 23].
Then, an artificial neural network model is made using training data sampled from numerical model.
A support vector machine learning algorithm was used to train the model using the training set.
Two models were trained using training sets I and II.
Using the random forest model package (v 4.6-10 4.6-10(v 3.1.2), we created a random forest model using ouR training set data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com