Regression: Let's say, we want to perform linear regression on a dataset containing m examples and n features. Our output is a linear funcion as follows: Ti = W₁x₁,1 + W2X₁,2 ++Wnxin + b Now, if the error is E, then the gradient descent weight update rules should be as follows: W₁ = wi - A for i € {1,2, ..., n} dwi b=b-ASE For the following loss functions E, find and SE Swi 1. Mean Squared Error: 2. Sum of Squared Error: 4. Mean Absolute Error: m E = ²₁(yi - Yi)² m i=1 E = = Σ²1 (Yi-Yi) ² 3. Mean Squared Logged Error: Sometimes, y; and can be too large. So, we use the following loss function. 'δω. 1m m E = (logyi - log Ji)² E = 1 Yi - Yi| 15m m
Regression: Let's say, we want to perform linear regression on a dataset containing m examples and n features. Our output is a linear funcion as follows: Ti = W₁x₁,1 + W2X₁,2 ++Wnxin + b Now, if the error is E, then the gradient descent weight update rules should be as follows: W₁ = wi - A for i € {1,2, ..., n} dwi b=b-ASE For the following loss functions E, find and SE Swi 1. Mean Squared Error: 2. Sum of Squared Error: 4. Mean Absolute Error: m E = ²₁(yi - Yi)² m i=1 E = = Σ²1 (Yi-Yi) ² 3. Mean Squared Logged Error: Sometimes, y; and can be too large. So, we use the following loss function. 'δω. 1m m E = (logyi - log Ji)² E = 1 Yi - Yi| 15m m
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Answer question 4.
Transcribed Image Text:Regression: Let's say, we want to perform linear regression on a dataset containing m examples
and n features. Our output is a linear funcion as follows:
Ti = W₁xi,1 + W2x i,2+.......... + Wnxi¸n + b
Now, if the error is E, then the gradient descent weight update rules should be as follows:
2. Sum of Squared Error:
w₁ = w₁ - XE for i € {1,2,...,n}
b=b-X
dwi
SE
For the following loss functions E, find and S.
db
1. Mean Squared Error:
SE
dwi
4. Mean Absolute Error:
m
1
E = ²₁ (Yi - Yi)²
m
E = ²₁ (Yi - Y₁ )²
3. Mean Squared Logged Error: Sometimes, y; and y, can be too large. So, we use the following
loss function.
E = ₁ (log yi - log yi)²
m i=1
E = = 2²₁ |Yi - Yi|
m i=1
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