Here is a clean, academic English version of your text, suitable for a paper, report, or presentation. I keep the structure and meaning intact, but make the language formal and natural.
What does the F1 score mean here?
The F1 score is a composite evaluation metric that simultaneously accounts for Precision and Recall. It is particularly well suited for binary classification problems with class imbalance, such as malicious URL detection (malicious vs. legitimate URLs).
Definition of the F1 Score
Precision: among all URLs predicted as malicious, the proportion that are truly malicious
\text{Precision}=\frac{TP}{TP+FP} $$ Recall: among all truly malicious URLs, the proportion that are correctly detected\text{Recall}=\frac{TP}{TP+FN}
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The F1 score is then defined:
If either Precision or Recall is low, the F1 score will be low
How is F1 used in your system design?
1) Rule-based system: threshold selection using validation F1
A rule-based detector typically outputs a risk score (or a count of triggered rules), which must be converted into a binary decision using a threshold (e.g., score ≥ T → malicious).
“Threshold selected using validation F1 score”
This means that multiple candidate thresholds T are evaluated on a validation set. For each threshold, Precision, Recall, and F1 are computed, and the threshold that maximizes the F1 score is selected.
The rationale for using F1 is the trade-off inherent in threshold selection:
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Increasing the threshold generally reduces false positives (FP) and increases Precision, but increases false negatives (FN) and lowers Recall.
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Decreasing the threshold generally increases Recall, but also increases FP, lowering Precision.
The F1 score identifies a compromise point that balances these two competing objectives.
2) Machine learning system: Random Forest with balanced class weights
A Random Forest classifier typically outputs either:
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a predicted probability p(y=1), or
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a proportion of votes across trees (ranging from 0 to 1).
As with the rule-based system, a decision threshold (commonly 0.5 by default) can be applied, and this threshold can also be tuned using validation-set F1, although this is not explicitly stated in the description.
“Balanced class weights”
This indicates that the training process assigns a higher cost to misclassifying malicious URLs, encouraging the model to pay more attention to the minority (malicious) class. This approach helps avoid degenerate solutions where the classifier predicts all URLs as legitimate, which would yield high accuracy but very low Recall.
In practice, balanced class weights often increase Recall, but may also increase false positives, which is why F1 score or precision–recall (PR) curves are commonly used for evaluation and threshold selection.
What is the role of F1 in malicious URL detection?
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If the primary concern is false positives, Precision (or FPR, or Precision at a fixed Recall) is more informative.
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If the primary concern is false negatives, Recall is more informative.
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If both types of errors are undesirable and neither can be ignored, F1 provides a widely used compromise metric.
It is important to note that F1 does not explicitly encode asymmetric costs between false positives and false negatives; it represents a mathematical balance rather than a cost-sensitive objective. Therefore, when false positives are a central concern, it is recommended to report additional metrics such as:
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Precision, Recall, and F1
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False Positive Rate (FPR) or false positives per thousand legitimate URLs
If you provide the confusion matrix (TP, FP, FN, TN) from your test set, I can compute the exact F1 score numerically for you (with or without derivation, as you prefer).