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Ferdi
Ferdi
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In a polynomial regression process(gradient descent) try to find the global minima to optimize the cost function. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)$\frac{Sr(m)}{n-m-1}$

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

  • Sr(m) = sum of the square of the residuals for the mth order polynomial
  • n= number of data points
  • m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

In a polynomial regression process(gradient descent) try to find the global minima to optimize the cost function. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

In a polynomial regression process(gradient descent) try to find the global minima to optimize the cost function. We choose the degree of polynomial for which the variance as computed by

$\frac{Sr(m)}{n-m-1}$

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

  • Sr(m) = sum of the square of the residuals for the mth order polynomial
  • n= number of data points
  • m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

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Reeves
Reeves
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In a polynomial regression we alwaysprocess(gradient descent) try to find the global minima whereto optimize the cost is minimal, so the residual must be tends to zerofunction. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

In a polynomial regression we always try to find the global minima where the cost is minimal, so the residual must be tends to zero. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

In a polynomial regression process(gradient descent) try to find the global minima to optimize the cost function. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/

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Reeves
Reeves
  • 226
  • 2
  • 5

In a polynomial regression we always try to find the global minima where the cost is minimal, so the residual must be tends to zero. We choose the degree of polynomial for which the variance as computed by

Sr(m)/(n-m-1)

is a minimum or when there is no significant decrease in its value as the degree of polynomial is increased. In the above formula,

Sr(m) = sum of the square of the residuals for the mth order polynomial

n= number of data points m=order of polynomial (so m+1 is the number of constants of the model)

Refer : https://autarkaw.org/2008/07/05/finding-the-optimum-polynomial-order-to-use-for-regression/