#0117

Binary Confusion Matrix With NumPy

MediumNumPy25 min20 XP

Problem

Given y_true and y_pred arrays containing 0/1 labels, return [tn, fp, fn, tp].

Why This Matters

Boolean masks are the NumPy way to express metric logic clearly and efficiently.

Function Signature

def binary_confusion_numpy(y_true, y_pred):

Examples

Example 1

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

Output[1,1,1,1]

There is one of each outcome type.

Constraints

Return the requested value exactly.
Handle the stated edge cases.
Keep the solution readable.

CodePython

import numpy as np

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

import numpy as np

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

There is one of each outcome type.

Hidden Test Categories

all correctall predicted positiveall predicted negative