Sentence examples for machine learning score from inspiring English sources

Notim.press/ed (a clever URL that reads "Not Impressed") lets you enter a URL and instantly see a site's Alexa top websites ranking, sentiment analysis, bounce rate, domain location, and a headline clickbait detection machine learning score.

The range of machine learning score is [0, 100].

Integration of experiments by MCCP provides a machine learning score for all 7635 proteins (Supplementary Table 1), including the 5795 evaluation proteins and 1840 uncharacterized proteins.

Machine learning scoring functions consist of four main building blocks: descriptors, model, training set and test set.

ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines.

The lowest energy docking solutions were then subjected to all-atom structure optimization using a more accurate physical energy function and the interaction energy components of the final structure used as input to the machine learning scoring method.

A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100.

With the proposed variable selection method, a machine learning scoring system outperforms traditional risk stratification systems such as TIMI and MEWS.

Whereas STAS extracts lexical tokens that can be used to create categories and rules using an analysis model similar to open coding in grounded theory (Strauss and Corbin, 1998), SIDE must be provided with a previously scored training set of data in order to develop its machine learning scoring model.

In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method.

Given that (a) students' responses are known to be strongly influenced by the prompts provided to them and the item features in these prompts (see Nehm and Ha [2011]) and (b) students' language is used to build machine-learning scoring models, the success of EvoGrader's scoring algorithms will be dependent on the training corpus.