Getting the r-squared value using curve_fit

There seems to be some background about R² not being implemented directly in scipy.

You can use sklearn.metrics.r2_score.

From your example:

from sklearn.metrics import r2_score
popt, pcov = curve_fit(func, xFit, yFit)
y_pred = func(xFit, *popt)
r2_score(yFit, y_pred)

Computing :

The value can be found using the mean (), the total sum of squares (), and the residual sum of squares (). Each is defined as:

where is the function value at point . Taken from Wikipedia.

From scipy.optimize.curve_fit():

• You can get the parameters (popt) from curve_fit() with

  popt, pcov = curve_fit(f, xdata, ydata)

• You can get the residual sum of squares () with

  • residuals = ydata- f(xdata, *popt)
  • ss_res = numpy.sum(residuals**2)

• You can get the total sum of squares () with

  ss_tot = numpy.sum((ydata-numpy.mean(ydata))**2)

• And finally, the -value with,

  r_squared = 1 - (ss_res / ss_tot)