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3 months ago in Engineering By Shreya K
How do you draw ROC and Precision-Recall (PR) curves for an SVM model?
I'm using an SVM for a classification task and want to evaluate it beyond simple accuracy. How do I actually generate ROC and Precision-Recall curves from the model's outputs, and what's the key practical difference in what these two curves tell me about my classifier's performance?
All Answers (2 Answers In All)
By Pravin Patel Answered 1 month ago
To draw curves, you need the model's prediction scores (not just class labels). In Python (scikit-learn), use `roc_curve` and `precision_recall_curve` functions with the true labels and SVM's `decision_function`/`predict_proba` outputs, then plot. Yes, RapidMiner can generate these curves automatically if configured to output prediction confidences. For installation issues, try Weka or Python as alternatives.
Replied 1 month ago
By Shreya K
Thank you, that’s really helpful! I see now that using prediction scores instead of class labels is key for drawing meaningful ROC or PR curves.
Reply to Pravin Patel
By Deepa S Answered 1 month ago
One practical tip: for SVMs with a linear kernel, use decision_function rather than predict_proba, since predict_proba isn’t always implemented unless you enable probability estimates during training (probability=True). After getting the scores, you can use matplotlib to plot fpr vs tpr for ROC or precision vs recall for PR. It’s also useful to calculate the area under the curve (AUC) with roc_auc_score or average_precision_score for a summary metric.
Reply to Deepa S
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