#0203

Binary Confusion Matrix

MediumML25 min20 XP

Problem

Return a dictionary with tp, fp, tn, and fn for binary labels where 1 is positive.

Why This Matters

Precision, recall, specificity, F1, and many interview questions all start from these four counts.

Function Signature

def confusion_counts(y_true, y_pred):

Examples

Example 1

Inputy_true = [1,0,1,0], y_pred = [1,1,0,0]

Output{"tp": 1, "fp": 1, "tn": 1, "fn": 1}

There is one case in each confusion-matrix cell.

Constraints

Return the exact requested result.
Handle common edge cases.
Keep the solution readable before optimizing.

CodePython

def confusion_counts(y_true, y_pred):
    """
    Input example: y_true = [1,0,1,0], y_pred = [1,1,0,0]
    Return: {"tp": 1, "fp": 1, "tn": 1, "fn": 1}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"tp": 0, "fp": 0, "tn": 0, "fn": 0}

def confusion_counts(y_true, y_pred):
    """
    Input example: y_true = [1,0,1,0], y_pred = [1,1,0,0]
    Return: {"tp": 1, "fp": 1, "tn": 1, "fn": 1}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"tp": 0, "fp": 0, "tn": 0, "fn": 0}

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputy_true = [1,0,1,0], y_pred = [1,1,0,0]

Expected{"tp": 1, "fp": 1, "tn": 1, "fn": 1}

There is one case in each confusion-matrix cell.

Hidden Test Categories

empty or minimal inputrepeated valueslarger realistic input