#0104

TPR and FPR From Counts

EasyML15 min10 XP

Problem

Given tp, fp, tn, fn, return {"tpr": tp/(tp+fn), "fpr": fp/(fp+tn)}. If a denominator is zero, return 0 for that rate.

Why This Matters

ROC curves and threshold tuning are built from TPR and FPR. The formulas need to become automatic.

Function Signature

def rates_from_counts(tp, fp, tn, fn):

Examples

Example 1

Inputtp=8, fp=2, tn=6, fn=4

Output{"tpr": 0.666..., "fpr": 0.25}

TPR uses actual positives; FPR uses actual negatives.

Constraints

Return the exact structure requested.
Handle the edge cases mentioned in the prompt.
Prefer clear logic over clever one-liners.

CodePython

def rates_from_counts(tp, fp, tn, fn):
    """
    Input example: tp=8, fp=2, tn=6, fn=4
    Return: {"tpr": 0.666..., "fpr": 0.25}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"tpr": 0, "fpr": 0}

def rates_from_counts(tp, fp, tn, fn):
    """
    Input example: tp=8, fp=2, tn=6, fn=4
    Return: {"tpr": 0.666..., "fpr": 0.25}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"tpr": 0, "fpr": 0}

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputtp=8, fp=2, tn=6, fn=4

Expected{"tpr": 0.666..., "fpr": 0.25}

TPR uses actual positives; FPR uses actual negatives.

Hidden Test Categories

zero positiveszero negativesperfect classifier