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Consider linear regression where y is our label vector, X is our data matrix, w is our model weights and o² is a measure of variance Using the squared error cost function has a probabilistic interpretation as: Maximising the probability of the model predicting the input data, assuming our input data follows a Normal distribution N(X; Xw, 0²) Maximising the probability of the model predicting the input data given the weights N(X; wy, o²) O Minimising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²) Maximising the values of the weights to minimise the input data N (y; w, σ²) Maximising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, 0²)

Consider linear regression where y is our label vector, X is our data matrix, w is our model weights and o² is a measure of variance Using the squared error cost function has a probabilistic interpretation as: Maximising the probability of the model predicting the input data, assuming our input data follows a Normal distribution N(X; Xw, 0²) Maximising the probability of the model predicting the input data given the weights N(X; wy, o²) O Minimising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²) Maximising the values of the weights to minimise the input data N (y; w, σ²) Maximising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, 0²)

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Consider linear regression where y is our label vector, X is our data matrix, w is our model weights and o² is a measure of variance Using the squared error cost function has a probabilistic interpretation as: O O O Maximising the probability of the model predicting the input data, assuming our input data follows a Normal distribution N(X; Xw, o²) Maximising the probability of the model predicting the input data given the weights N(X; wy, o²) Minimising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²) Maximising the values of the weights to minimise the input data N (y; w, o²) Maximising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²)
Transcribed Image Text:Consider linear regression where y is our label vector, X is our data matrix, w is our model weights and o² is a measure of variance Using the squared error cost function has a probabilistic interpretation as: O O O Maximising the probability of the model predicting the input data, assuming our input data follows a Normal distribution N(X; Xw, o²) Maximising the probability of the model predicting the input data given the weights N(X; wy, o²) Minimising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²) Maximising the values of the weights to minimise the input data N (y; w, o²) Maximising the probability of the model predicting the labels, assuming our prediction errors follow a Normal distribution N(y; Xw, o²)
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