Sentence examples for using the Matthews correlation from inspiring English sources

Predictive performance was measured using the Matthews correlation coefficient (MCC, (Matthews, 1975)): M C C = T p T n − F p F n (T p + F n ) (T p + F p ) (T n + F p ) (T n + F n ).

We assessed prediction performance using the Matthews Correlation Coefficient for binary data (MCC, eq. 4).

We summarized the overall prediction accuracy of a model and method using the Matthews Correlation Coefficient (MCC) [ 40].

On the 80 % training set we used 10-fold cross-validation to select the best prior regularization parameters (L 1 or L 2 and C) using the Matthews correlation coefficient (MCC) as a balanced performance measure [ 72], and the prediction cut-off p c   = 0.5.

We used the Matthews Correlation Coefficient (MCC) to quantify the balance between sensitivity and specificity [ 34].

The weight parameters were optimized by changing them so that the Matthews correlation coefficient (Matthews 1975), MCC, was maximized.

We have used the following measurements to evaluate the classification accuracy of a model: sensitivity (Sn), specificity (Sp), the overall accuracy (Ac) and the Matthews correlation coefficient (MCC) (Matthews, 1975).

For the classification models the Matthews correlation coefficient (MCC) was used to estimate model performance as it incorporates both correct and false predictions [62].

For the second metric, we used the results of the same runs in order to calculate the Matthews Correlation Coefficient (MCC) [29], a measure of prediction success.

Previous work [34] using the naive Bayes classifier has shown that optimising the threshold probability score based on the Matthews Correlation Coefficient can lead to better virtual screening performance.

Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure.