the least square functions could not applied to logistic regression problems. provide the reasons why least square loss is not applicable
Added by Jes-S P.
Step 1
g., 0 or 1). The model predicts the probability that a given input belongs to a particular category. Show more…
Show all steps
Your feedback will help us improve your experience
Sri K and 73 other AP CS educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
The first formula is the loss function of the linear regression model. L(w,b) = ̑ (y(i) - h(x(i)))^2 where h(x) = wx + b. The loss function of the logistic regression model is: L(w,b) = ̑ y(i) log(h(z(i))) + (1 - y(i)) log(1 - h(z(i))) . (2) i=1 where h(z) = 1 / (1 + e^(-z)). Please prove that although both models have different loss functions, their optimizations are the same since they have the same derivatives: ∂L(w,b) / ∂w = 2̑ (h(x(i)) - y(i))x(i) and ∂L(w,b) / ∂b = 2̑ (h(x(i)) - y(i)).
Sri K.
8. (1pt) TRUE or FALSE, and explain why in one sentence. If the standard regression assumptions do not hold, then regression will not minimize the sum of the squares of the residuals.
Ameer S.
How is it that a logistic regression is able to use a logistic curve, which is non-linear in probabilities, in order to estimate a linear relationship between two variables? What problem will occur in our estimates if we were to run an OLS regression on data that requires a logistic regression model?
Adi S.
Recommended Textbooks
Computer Science and Information Technology
Introduction to Programming Using Python
Computer Science - An Overview
Transcript
18,000,000+
Students on Numerade
Trusted by students at 8,000+ universities
Watch the video solution with this free unlock.
EMAIL
PASSWORD