Logistic regression aims to train the parameters from the training set D = {(x(¹), y(¹)), i = 1,2,...,m, y = {0, 1}} so that the hypothesis function h(x) = g(0¹ x) 1 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0₁ = 0; + α(y(¹) — hz ( x (¹))) x (D) (here g(z) is the logistic or sigmod function g(z) = ) can predict the probability of a
Logistic regression aims to train the parameters from the training set D = {(x(¹), y(¹)), i = 1,2,...,m, y = {0, 1}} so that the hypothesis function h(x) = g(0¹ x) 1 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0₁ = 0; + α(y(¹) — hz ( x (¹))) x (D) (here g(z) is the logistic or sigmod function g(z) = ) can predict the probability of a
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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![Logistic regression aims to train the parameters from the training set D =
g(0¹ x)
{(x(i),y(¹)), i = 1, 2,...,m, y € {0, 1}} so that the hypothesis function h(x)
1
(here g(z) is the logistic or sigmod function g(z)
1+ e-z
new instance x being labeled as 1. Please derive the following stochastic gradient ascent
update rule for a logistic regression problem.
0₁ = 0₁ + α(y(¹) - h₂(x)))x)
=
) can predict the probability of a](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb6d57dbf-1951-461b-ba86-1f9bc3e1f7b9%2Fd51b5206-9498-48ca-a7be-96e19c7c9db3%2Fmq2hpuc_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Logistic regression aims to train the parameters from the training set D =
g(0¹ x)
{(x(i),y(¹)), i = 1, 2,...,m, y € {0, 1}} so that the hypothesis function h(x)
1
(here g(z) is the logistic or sigmod function g(z)
1+ e-z
new instance x being labeled as 1. Please derive the following stochastic gradient ascent
update rule for a logistic regression problem.
0₁ = 0₁ + α(y(¹) - h₂(x)))x)
=
) can predict the probability of a
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