Pattern Recognition Lecture 2: Linear Regression II
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Pattern Recognition Lecture 2: Linear Regression II

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Questions and Answers

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

  • To minimize the cost function (correct)
  • To add noise to the data
  • To make the model more complex
  • To maximize the cost function

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

  • The cost function (correct)
  • The learning rate
  • The parameters of the model
  • The number of iterations

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

  • Learning rate determines the number of iterations
  • Higher learning rate leads to faster convergence (correct)
  • Lower learning rate leads to faster convergence
  • Learning rate has no impact on 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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