Linear Regression TP 1 Part 2: Multivariate Linear Regression
29 Questions
3 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What is the primary goal of regression techniques in machine learning?

  • To identify patterns in data
  • To classify data into categories
  • To predict a continuous variable (correct)
  • To predict a categorical variable

    • Which regression technique is most suitable for capturing non-linear relationships between variables?

  • Ridge Regression
  • Linear Regression
  • Polynomial Regression (correct)
  • Neural Networks

    • What is the primary advantage of Lasso and Ridge Regression?

  • They are computationally efficient
  • They are easy to interpret
  • They can model complex relationships
  • They can avoid overfitting (correct)

    • Which of the following is a disadvantage of Neural Networks?

    <p>They are difficult to interpret</p> Signup and view all the answers

    What is a disadvantage of Polynomial Regression?

    <p>It can easily overfit the data</p> Signup and view all the answers

    What is an advantage of Linear Regression?

    <p>It is simple to understand and interpret</p> Signup and view all the answers

    What is the initial value of the parameter w?

    <p>0</p> Signup and view all the answers

    What is the value of the partial derivative of the cost function with respect to w after the first iteration?

    <p>-650</p> Signup and view all the answers

    What is the cost function used in the linear regression algorithm?

    <p>2n ∑(y - y)^2</p> Signup and view all the answers

    What is the value of the parameter b after the second iteration?

    <p>7.8625</p> Signup and view all the answers

    What is the purpose of the gradient descent algorithm in linear regression?

    <p>To find the optimal values of the parameters w and b</p> Signup and view all the answers

    What is the value of the cost function after the second iteration?

    <p>79274.8125</p> Signup and view all the answers

    What are some challenges associated with linear regression models?

    <p>Being difficult to interpret and requiring large datasets</p> Signup and view all the answers

    What is the purpose of the bias term 'b' in linear regression?

    <p>To shift the regression line</p> Signup and view all the answers

    What does the weight 'w' represent in linear regression?

    <p>The strength of the relationship between input and output variables</p> Signup and view all the answers

    What is meant by multivariate regression?

    <p>Regression with multiple input variables</p> Signup and view all the answers

    What is the dependent variable in linear regression?

    <p>y, the output variable</p> Signup and view all the answers

    What is the equation for simple linear regression?

    <p>y = wx + b</p> Signup and view all the answers

    When combining multiple features, such as surface and number of chambers, what is the potential issue that needs to be addressed?

    <p>Features with different scales</p> Signup and view all the answers

    What technique is used to encode a feature with discrete values, such as the type of a house?

    <p>OneHotEncoder</p> Signup and view all the answers

    What is the primary benefit of applying Principal Component Analysis (PCA) to a dataset with multiple features?

    <p>Reducing feature correlation</p> Signup and view all the answers

    What is the purpose of using StandardScaler in regression analysis?

    <p>To normalize features with different scales</p> Signup and view all the answers

    When dealing with a dataset with multiple correlated features, what technique can be used to reduce feature dimensionality?

    <p>Principal Component Analysis (PCA)</p> Signup and view all the answers

    What is the primary function of the gradient in the linear regression algorithm?

    <p>To update the parameters w and b</p> Signup and view all the answers

    What is the purpose of the seuil de tolérance in the linear regression algorithm?

    <p>To determine the convergence of the algorithm</p> Signup and view all the answers

    What is the formula for the mean squared error (MSE) in the linear regression algorithm?

    <p>1/n * ∑(y(i) - y^(i))^2</p> Signup and view all the answers

    What is the role of the parameter w in the linear regression algorithm?

    <p>It is the weight parameter</p> Signup and view all the answers

    What is the purpose of the initial step in the linear regression algorithm?

    <p>To initialize the parameters w and b with random values</p> Signup and view all the answers

    What is the condition to stop the algorithm in the linear regression algorithm?

    <p>When the cost function is below the seuil de tolérance</p> Signup and view all the answers

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser