Logistic regression gradient descent

Logistic regression loss function vs. cost function

Logistic Regression Cost Function

A machine learning model is trained for a binary classification task where the goal is to predict a label y (either 0 or 1). The model's prediction, ŷ, is a probability between 0 and 1. The performance on a single example is measured using the loss function: L(ŷ, y) = -(y*log(ŷ) + (1 - y)*log(1 - ŷ)).

Consider two scenarios for an example where the true label y is 1:

• Scenario A: The model predicts ŷ = 0.9.
• Scenario B: The model predicts ŷ = 0.1.

Which scenario results in a higher loss value, and why?

When training a logistic regression model for binary classification, the standard approach is to use the logarithmic loss function: L(ŷ, y) = -(y*log(ŷ) + (1 - y)*log(1 - ŷ)). An alternative could be the squared error loss: L(ŷ, y) = (ŷ - y)². What is the primary reason the logarithmic loss is preferred for this task?

Calculating Loss for a Single Prediction

Logistic regression loss function vs. cost function

(Batch) Gradient Descent (Deep Learning Optimization Algorithm)

True or False: The cost function for logistic regression trained with m≥1 examples is always greater than or equal to zero.