The metric to focus on depends on the data mining goal you are trying to achieve. It is important to use cross-validation to detect if over-fitting is taking place. Cross-validation takes a dataset and splits it randomly (or using a stratified sampling) into multiple validation datasets. Then the models creates its predictions using these validation sets to calculate the metrics for each dataset. If overfitting takes place, the metrics of your model between these validation datasets will differ substantially. If it does not take place, the metrics between the validation datasets should be similar.
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