Sentence examples for value of a feature from inspiring English sources

This criterion is defined as the maximum accuracy achievable when each value of a feature predicts a different class value.

By that the value of a feature is unclear, as is whether a picked feature does not have a counter-part of similar characteristics that was not picked, as only one of a sort is needed.

Information gain is one of the most common feature selection methods for sentiment analysis [3, 9, 19, 35], which measures the content of information obtained after knowing the value of a feature in a document.

The metafeatures are found for this method by computing a feature-label co-occurrence value for each feature from the source and target space by calculating the expected value of a feature based on the given label from the labeled training data.

For each time interval, the value of a feature is given by its usage statistic for the corresponding term.

In general, the absolute value of a feature score is proportional to its contribution to the SVM classification.

In this model, we distinguish two kinds of textual values of a feature: First, the feature value is already in the form of a set of expressions, such as the value of features tags, category, sentiment, and emotion.

The challenge is actually twofold: Firstly, a vector should not contain an abundant amount (thousands or hundreds) of features or values of a feature, because of the curse of dimensionality [32]; secondly, every vector should have the same number of dimensions, in order to fit the classifiers.

The importance is obtained by randomly permuting the values of a feature and measuring the resulting decrease in classification accuracy.

The proposed use of abundantly available cross-species data is analogous to sampling the missing values of a feature from another related feature distribution.

We test the significance of the difference of the values of a feature in the hot spot and non hot spot residues.