"training of data" is a valid phrase that can be used in written English.
It refers to the process of preparing a dataset for use in a machine learning algorithm or other data analysis methods. Example: The success of the predictive model relied heavily on the accuracy and thoroughness of the training of data.
The model was first build with training dataset and 5-fold cross validation was used during training of data.
The CALM guide can be used for training of data collectors using simulations prior to initiating any study involving higher-risk dyads.
Figures 9, 10, 11, 12, and 13 show the results for the prediction for the subsequent half-year's data traffic load based on training of data traffic load of the first half of the year (Experiment A2).
However, with the effective training of data collectors on probing mechanisms, this was minimized.
The final stage in the training of data collectors involved field-testing of data collection tools.
SOUL - a Yemeni National NGO coordinated the data collection and training of data collectors.
All data were first normalized and divided into two datasets such as: training (of all data), test (of all data).
Each SVM was trained with the use of a training set of data points of known labels (in this case, genes with Gene Ontology Biological Processes (GO‐BP) annotation).
The data are divided into training (60% of data), validation (20% of data), and test sets (20% of data).
Then by using the same law of training sets of data, the SVM model can give appropriate output of testing sets of data.
Figure 22 Training of the data traffic rates of the day before for prediction of mobile traffic data load for particular day. Figure 23 Relative error from training of the data traffic load of the day before.
