Sentence examples for using a training subset from inspiring English sources

In a balanced cohort of 76 patients, a neural network-based prediction model was built using a training subset of the cohort to first identify proteomic patterns of VTE.

The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data.

Typically, when a diagnostic model is built upon a set of perspective markers, elaborated by using a "training" subset of data, a common practice to estimate its performance consists in feeding the model with randomly selected data ("testing" subset) and examining its ability to correctly classify these data as belonging to either of the two groups (e.g., healthy and pathological).

A training subset has been used to evaluate and adjust antaRNA's parameters.

The training set is randomly split into a training subset (80%% of the data used to train the models) and evaluation subset (20%% of the recordings employed to evaluate the models) in order to provide insights into the performance of both C 50 estimators.

Each tree is constructed using a subset of training data and a subset of variables.

Equation (6) is used by SVM to produce a function h (Eq. 3) using the training subset, whereas the function h is evaluated (Eqns. 4 and 5) using the test subset.

The set of statistical parameters λ i and ψ i was determined using the training subset z i that consists of all the data, apart from the testing subset x i.

We iteratively split the entire dataset into ten equal parts and executed each algorithm the same number of times, using a different subset as training set (S) while the rest of the sets, unified, comprised the testing set (R).

Using five bins provides the best balance between having sufficient bins to distinguish quality levels, while maintaining enough bases within each quality bin that the HMM can be adequately trained using a random subset of reads.

A RF classifier is composed of a large number of decision trees, each trained using a random subset of samples (bagging) and constructed by selecting the best splitting features from random and independent sampled subsets of features [ 20, 21].