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License: MIT License
Automatic gemorphological classification based on Digital Geomorphological Maps of Poland
License: MIT License
If the classifier is underfitted (too generalized) then we can't calculate the performance metrics because of the missing class in the prediction. We should improve this aspect in the future as follows: If class can't be predicted, assign it value of -1 and calculate the metrics. Then create a separate attribute with information how many classes are missing. Another problem is the differences in the occurrence of classes in the training and test sets.
Here is the current method:
num_class = length(unique(train$class))
pred_class = predict(model, test[, -1])
if (length(levels(pred_class)) != num_class) {
accuracy = NA
} else {
accuracy = accuracy_vec(test$class, pred_class)
}
XGBoost provides its own interface for crossvalidation by xgb.cv() function. Maybe we can replace some code in 04_maps_crossvalidation.R. In addition, there is slice() function for selecting data from xgb.DMatrix
(instead of data frame), so some code can be simplified too.
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