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Impara Challenge: Optimising Function Of Multiple Variables | Mathematical Analysis
Mathematics for Data Analysis and Modeling

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Challenge: Optimising Function Of Multiple Variables

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The most commonly used loss function in linear regression is the Mean Squared Error (MSE) loss function. This function is the squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Soluzione

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Sezione 3. Capitolo 7
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Challenge: Optimising Function Of Multiple Variables

Compito

Swipe to start coding

The most commonly used loss function in linear regression is the Mean Squared Error (MSE) loss function. This function is the squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

close

Awesome!

Completion rate improved to 4.76

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