Linear Regression TP 1 Part 2: Multivariate Linear Regression

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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?

They are difficult to interpret Signup and view all the answers

What is a disadvantage of Polynomial Regression?

It can easily overfit the data Signup and view all the answers

What is an advantage of Linear Regression?

It is simple to understand and interpret Signup and view all the answers

What is the initial value of the parameter w?

0 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?

-650 Signup and view all the answers

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

2n ∑(y - y)^2 Signup and view all the answers

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

7.8625 Signup and view all the answers

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

To find the optimal values of the parameters w and b Signup and view all the answers

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

79274.8125 Signup and view all the answers

What are some challenges associated with linear regression models?

Being difficult to interpret and requiring large datasets Signup and view all the answers

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

To shift the regression line Signup and view all the answers

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

The strength of the relationship between input and output variables Signup and view all the answers

What is meant by multivariate regression?

Regression with multiple input variables Signup and view all the answers

What is the dependent variable in linear regression?

y, the output variable Signup and view all the answers

What is the equation for simple linear regression?

y = wx + b 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?

Features with different scales Signup and view all the answers

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

OneHotEncoder Signup and view all the answers

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

Reducing feature correlation Signup and view all the answers

What is the purpose of using StandardScaler in regression analysis?

To normalize features with different scales Signup and view all the answers

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

Principal Component Analysis (PCA) Signup and view all the answers

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

To update the parameters w and b Signup and view all the answers

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

To determine the convergence of the algorithm Signup and view all the answers

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

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

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

It is the weight parameter Signup and view all the answers

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

To initialize the parameters w and b with random values Signup and view all the answers

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

When the cost function is below the seuil de tolérance Signup and view all the answers