The phrase "a set of training points" is correct and usable in written English.
You can use it when referring to a collection of data points used for training a model or algorithm, particularly in machine learning or statistics.
Example: "To improve the accuracy of our predictive model, we need to gather a set of training points that represent various scenarios."
Alternatives: "a collection of training data" or "a group of training samples."
Because the feature maps based on PD/T2w images used for initial classification were generated from a set of training data points, it is essential to normalize intensity distribution of input image volume to that of the training data set.
The transformation requires a set of training data points with different labels.
Overfitting could occur when three hemoglobin predictors, as well as pretreatment optical scatter data, in addition to tumor pathological variables were used as predictor variables to fit a limited set of training data points.
The number of training points are chosen to be small to keep the small sample setting, and to have a large enough testing set.
As each class/annotation term has a set of associated training data points, which convey the same biological record information as the class/annotation term, we consider both a class/annotation term vertex and its labeled training image bag vertices as a group set, (4) which is illustrated as the vertices with orange boundary in 3.
For training purposes, a set of 14451 points was assembled.
As the basis for the GISPEX approach we generated a set of random points serving as input training data.
Given a training data D, a set of n points of the form (2) D = x i, y i ∣ x i ∈ R, y i ∈ − 1,1 i = 1 n, where x i is the reads count in each bin around the ith gene's TSS and the y i is either 1 or −1, indicating the two classes to be classified as the real transcription start sites versus random genomic regions.
The timetable is designed by choosing the time of service for customer unit train demands among a set of discrete points.
Usually, one is interested in a high prediction accuracy on data not available during the optimization process, that is, one wants a function that generalizes well beyond the given set of training data points.
