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There is an equation that describes the cost function of Ridge, where j runs from 0 to p. So if p is 3, it means the linear model reads y=w0 + w1*xl+w2*x2 +w3*x3. wo is the intercept or rather bias, wl, w2 and w3 makes the slope of the plane. Now, assume p= 1, so this turns into your usual single-variate linear model. Work out the gradient descent by hand. M Σ (n − w)² = Ï (n − Ĺ w₁ x zu )³² +1 Σω - +) i=1 j=0 j=0 Cost function for ridge regression

There is an equation that describes the cost function of Ridge, where j runs from 0 to p. So if p is 3, it means the linear model reads y=w0 + w1*xl+w2*x2 +w3*x3. wo is the intercept or rather bias, wl, w2 and w3 makes the slope of the plane. Now, assume p= 1, so this turns into your usual single-variate linear model. Work out the gradient descent by hand. M Σ (n − w)² = Ï (n − Ĺ w₁ x zu )³² +1 Σω - +) i=1 j=0 j=0 Cost function for ridge regression

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 93E
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PLEASE SHOW WORK IN CLEAR HANDWRITING. MESSY HANDWRITING IS NOT ALLOWED
a) There is an equation that describes the cost function of Ridge, where j runs from 0 to p. So if p is 3, it y=w0+wl*xl+w2*x2+w3*x3. w0 is the intercept or rather bias, wl, w2 means the linear model reads and w3 makes the slope of the plane. Now, assume p= 1, so this turns into your usual single-variate linear model. Work out the gradient descent by hand. P Σ( - ² - Σ (n -Ĺ‚× 2)² + x Ĺ² j=0 Cost function for ridge regression b) With the gradient descent, now please write down update rule A simple update rule • dy = f(x+h)-f(x). . dy=dxdx Then, f(x + h) = dy+dx+f(x) • Or rather, f(x₁) = x+dx+f(x₂)
Transcribed Image Text:a) There is an equation that describes the cost function of Ridge, where j runs from 0 to p. So if p is 3, it y=w0+wl*xl+w2*x2+w3*x3. w0 is the intercept or rather bias, wl, w2 means the linear model reads and w3 makes the slope of the plane. Now, assume p= 1, so this turns into your usual single-variate linear model. Work out the gradient descent by hand. P Σ( - ² - Σ (n -Ĺ‚× 2)² + x Ĺ² j=0 Cost function for ridge regression b) With the gradient descent, now please write down update rule A simple update rule • dy = f(x+h)-f(x). . dy=dxdx Then, f(x + h) = dy+dx+f(x) • Or rather, f(x₁) = x+dx+f(x₂)
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