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What Does High Accuracy Low Recall High Precision Mean for No-Code ML Models?
High accuracy with low recall and high precision in no-code ML signals class imbalance and missed positives; learn diagnostic steps, threshold tuning, resampling fixes, and validation actions to ensure reliable real-world deployment.
Question
Your no-code model shows an accuracy of 94%, but recall is significantly lower than precision. What does this combination of metrics suggest about your model’s performance, and what actions should you take before deploying it in a real-world environment?
Answer
High accuracy (94%) with significantly lower recall than precision suggests class imbalance in the dataset, where the model excels at identifying the majority (negative) class but misses many true positive instances, often prioritizing conservative predictions to maintain high precision while achieving inflated accuracy through dominance of the negative class. This imbalance indicates potential overfitting to prevalent patterns or a high decision threshold that favors few positive predictions, risking critical business failures like undetected fraud, diseases, or defects where false negatives carry high costs, thus rendering the model unreliable despite the appealing accuracy figure.
Before real-world deployment, take these actions: analyze the confusion matrix to quantify false negatives; adjust the classification threshold downward via ROC curve analysis to boost recall at precision’s expense; resample data with techniques like SMOTE for minority class oversampling or undersample the majority class; compute and prioritize the F1 score or business-aligned metric like expected value loss; perform stratified cross-validation on held-out data to confirm generalization; and test under simulated production conditions including drift scenarios. Document trade-offs and pilot with a subset of live traffic to validate improvements without full exposure.