#0010

Build a Confusion Matrix

EasyML15 min10 XP

Problem

Given true binary labels and predicted binary labels, return a dictionary with TP, TN, FP, and FN counts.

Why This Matters

Accuracy can hide failure. Confusion matrix counts show exactly which kind of mistake the model is making.

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, "TN": 1, "FP": 1, "FN": 1}

Each of the four possible classification outcomes happens once.

Constraints

Labels are 0 or 1.
y_true and y_pred have the same length.
Return integer counts.

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, "TN": 1, "FP": 1, "FN": 1}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    counts = {"TP": 0, "TN": 0, "FP": 0, "FN": 0}
    # write your code here
    return counts

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

Visible browser tests run here when available.

Testcases1 visible / 4 hidden categories

All outcome types

Input[1, 0, 1, 0], [1, 1, 0, 0]

Expected{"TP": 1, "TN": 1, "FP": 1, "FN": 1}

This test ensures every branch is handled.

Hidden Test Categories

All predictions correctAll predictions positiveNo positive true labelsLonger lists