Sentence examples for predictions to train from inspiring English sources

The difference is that: MMR uses the validation dataset to get the predictions from these base classifiers and then use these predictions to train a meta-learner algorithm; Adaboost.PL sorts the hypotheses generated from all the iterations from each computing node and then by merging them together a final classifier is generated.

A precondition for interpretable predictions is to train and apply the model with explicable and meaningful features.

The classifier uses CPSC and PPAC predictions as evidence to train the predictive model using the Random Forest algorithm (49), and outputs the predicted risk of ccRCC in a binary classification scheme (high or low risk).

However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.

Short read and/or assembled transcript alignments are typically used to correct the coordinates of intron-exon boundaries in existing gene annotations or predictions [ 7], to train gene predictors [ 8], and can also be incorporated directly into gene prediction by hybrid gene predictors [ 9, 10].

Another way to compare two prediction methods is to train and test on the same data set.

For predictions between environments, adjusted data from two environments were used to train predictions in the remaining three environments.

It is important to note that the output layer predictions are only necessary to train the network and we are not interested in them otherwise.

To run the gene prediction it was necessary to train GeneMark on S. cerevisiae by building a matrix with transition probabilities for coding and non-coding regions used by the Hidden Markov Model (HMM) of the program.

In machine learning such as SVM where the goal is to classify samples, the "Gold Standard" refers to a set of data that can be used to train the prediction model and to test predictions.

In reality, it is common practice to train a prediction model based on effort datasets to predict the effort required by a project.