Suppose that in a binary classification task, I have separate classifiers A, B, and C. If I use A alone, I will get a high precision, but low recall. In other words, the number of true positives are very high, but it also incorrectly tags the rest of the labels as False. B, and C have much lower precision, but when used separately, they may (or may not) result in better recall. How can I define an ensemble classifier that gives precedence to classifier A only in cases where it labels the data as True and give more weight to the predictions of other classifiers when A predicts the label as False.

The idea is, A is already outperforming others in catching true positives and I only want to improve the recall without hurting precision.

  • 4
    Ensemble models learn the correct weights for you. Read about boosting and stacking. You can tune the ensemble classifier to yield the recall/precision trade-off you desire. Welcome to the site! – Emre Jan 11 at 19:27
  • can you describe the data? what kind of classifiers you are using? – Bashar Haddad Apr 12 at 0:35

Feature-Weighted Linear Stacking might be what you are looking for.

FWLS combines model predictions linearly using coefficients that are themselves linear functions of meta-features.

In your example you can use the meta-feature "Does A label the example as True?"

based on your description, it looks like different models have different biases. two important questions: do you have any data imbalance problem? what kind of models you are using? using stacking based classifier is beneficial if you have different biases. Try to use a simple stack based classifier. for your level-1 classifier, use different models (e.g. SVM-L, SVM-NL, DT, RF, ... etc). For your meta-data, use probabilities and for the meta-classifier use Random Forest.

if you have data imbalance problem using stack based classifier is a little bit more challenging.

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