The phrase "average classification error" is correct and usable in written English.
It is typically used in the context of machine learning or statistics to describe the average rate at which a classification model makes incorrect predictions.
Example: "The model's performance was evaluated using the average classification error, which indicated a need for further tuning."
Alternatives: "mean classification error" or "typical classification error".
The random splitting is repeated 20 times, and the average classification error rate and the average number of selected feature subsets are used to estimate the fitness function.
The average classification error obtained was 2.1percentnt.
Since the average classification error is the mean of the two error rates, a majority-classifier that classifies most of the samples as positives would get a high false positive rate and thus a high average classification error.
(3) In order to assess every model in more accurate manner, cross-validation (usually tenfold) is used and average classification error is computed.
The average classification error in standard and reversed cross-validation scheme were very close to each other, in all cases the error is only 1percentnt higher in the reversed setup.
The fitness of the individual that represented the solution Φ i was then defined as the average classification error achieved in the 10-fold cross validation when the selected features Φ⊚ X(C ) were used.
The average classification errors for subject one were ~21.75 with four channels and ~28.28 with three channels for tasks one and two, respectively.
Note that, for single-label classification problems, the one-error is identical to ordinary classification error.
Sets of variables (i.e. expression ratios) in the range of 2 to 30 were randomly drawn one thousand times each from the preselected data set and the averaged classification errors were calculated.
Table 1 Image reconstruction and classification performance of DP-MCS, MDL-MCS, and MDL-LMCS Average reconstruction Correct classification error ratio Linear 0.22623 - DP-MCS 0.27647 0.35 MDL-MCS 0.24511 0.60 MDL-LMCS 0.22642 1.0.22642
The average classification and prediction errors were the averages across all cross-validation intervals and all runs [ 19].
