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# Pattern Recognition Lecture 2: Linear Regression II

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@EnjoyableSaxhorn

### What is the purpose of the Gradient Descent algorithm in linear regression?

To minimize the cost function

### In the context of linear regression, what does J(θ₀,θ₁) represent?

The cost function

### How does the learning rate affect the Gradient Descent algorithm?

Higher learning rate leads to faster convergence

### What is the relationship between the parameters θ₀ and θ₁ in linear regression?

<p>θ₀ represents the intercept and θ₁ represents the slope</p> Signup and view all the answers

### Which function does the Gradient Descent algorithm aim to optimize in linear regression?

<p>Cost function</p> Signup and view all the answers

### What is the significance of iterations in the Gradient Descent algorithm for linear regression?

<p>Iterations are needed to update the parameters towards convergence</p> Signup and view all the answers

### Which concept relates to finding the 'elbow' in linear regression?

<p>'Bowel-shaped' function</p> Signup and view all the answers

### How does adjusting the model parameters impact linear regression?

<p>It improves the cost function</p> Signup and view all the answers

### What is a key characteristic of a good learning rate in Gradient Descent for linear regression?

<p>High learning rate for stable convergence</p> Signup and view all the answers

### In linear regression, what is primarily updated during each iteration of the Gradient Descent algorithm?

<p>The model parameters like slope and intercept</p> Signup and view all the answers

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