#0014

Binary Cross Entropy Loss

MediumML20 min15 XP

Problem

Given binary labels and predicted probabilities, return the mean binary cross entropy. Clip probabilities to avoid log(0).

Why This Matters

Binary cross entropy is the loss behind logistic regression and many binary classifiers. It punishes confident wrong predictions heavily.

Function Signature

def binary_cross_entropy(y_true, y_prob):

Examples

Example 1

Inputy_true = [1, 0], y_prob = [0.9, 0.2]

Outputabout 0.1643

Both predictions are fairly confident and correct, so the average loss is low.

Constraints

y_true contains 0 and 1 labels.
y_prob contains probabilities between 0 and 1.
Return the mean loss as a float.

CodePython

import numpy as np

def binary_cross_entropy(y_true, y_prob):
    """
    Input example: y_true = [1, 0], y_prob = [0.9, 0.2]
    Return: about 0.1643
    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

import numpy as np

def binary_cross_entropy(y_true, y_prob):
    """
    Input example: y_true = [1, 0], y_prob = [0.9, 0.2]
    Return: about 0.1643
    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

Visible browser tests run here when available.

Testcases2 visible / 4 hidden categories

Mostly correct probabilities

Input[1, 0], [0.9, 0.2]

Expectedapproximately 0.1643

Correct confident predictions produce small loss.

Clips extreme probabilities

Input[1, 0], [1.0, 0.0]

Expectedfinite loss

Clipping avoids log(0).

Hidden Test Categories

Confident wrong predictionsAll positive labelsAll negative labelsLonger arrays