regression
Python natural smoothing splines
After hours of investigation, I did not find any pip installable packages which could fit a natural cubic spline with user-controllable smoothness. However, after deciding to write one myself, while reading about the topic I stumbled upon a blog post by github user madrury. He has written python code capable of producing natural cubic spline … Read more
How to calculate the regularization parameter in linear regression
The regularization parameter (lambda) is an input to your model so what you probably want to know is how do you select the value of lambda. The regularization parameter reduces overfitting, which reduces the variance of your estimated regression parameters; however, it does this at the expense of adding bias to your estimate. Increasing lambda … Read more
Quadratic and cubic regression in Excel
You need to use an undocumented trick with Excel’s LINEST function: =LINEST(known_y’s, [known_x’s], [const], [stats]) Background A regular linear regression is calculated (with your data) as: =LINEST(B2:B21,A2:A21) which returns a single value, the linear slope (m) according to the formula: which for your data: is: Undocumented trick Number 1 You can also use Excel to … Read more
fitting data with numpy
Unfortunately, np.polynomial.polynomial.polyfit returns the coefficients in the opposite order of that for np.polyfit and np.polyval (or, as you used np.poly1d). To illustrate: In [40]: np.polynomial.polynomial.polyfit(x, y, 4) Out[40]: array([ 84.29340848, -100.53595376, 44.83281408, -8.85931101, 0.65459882]) In [41]: np.polyfit(x, y, 4) Out[41]: array([ 0.65459882, -8.859311 , 44.83281407, -100.53595375, 84.29340846]) In general: np.polynomial.polynomial.polyfit returns coefficients [A, B, C] … Read more