Sentence examples for classifications and probabilities from inspiring English sources

SVM and random forest were used for the classifications, and probabilities are given for features without and with max-pooling (Mp).

Therefore, classifications and probabilities estimated by a logistic regression model are more likely to be accepted by clinicians than results obtained by machine learning methods, such as artificial neural networks or support vector machines although these generally may look quite impressive.

The use of an ensemble of trees by creating a forest generally leads to both improved classifications and probability estimates (Bauer and Kohavi 1999; Breiman 2001; Buntine 1992; Provost and Domingos 2003; Provost et al. 1998).

This task is performed in this paper from the point of view of the Theory of Statistical Learning, which provides a unified framework for all regression, classification and probability density estimation.

Therefore, it might be more meaningful to develop classification and probability estimation models using methods specifically targeted at classification and probability estimation.

In the following, we will focus on classification and probability estimation based on GWA data.

Specifically, association, classification and probability estimation can be different aims of studies, require different methods, and result in different interpretations.

In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules.

For this, we will describe in the next section how to construct and evaluate classification and probability estimation rules.

Instead, we should keep in mind that association, classification and probability estimation are different aims with their own values.

Moreover, when applied, their specific value with regard to classification and probability estimation has usually not been exhausted.