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What is wrong with the following code snippet for fitting a quadratic model using curve_fit?

medium📝 Debug Q14 of 15
SciPy - Curve Fitting and Regression
What is wrong with the following code snippet for fitting a quadratic model using curve_fit?
import numpy as np
from scipy.optimize import curve_fit

def quad(x, a, b, c):
    return a * x**2 + b * x + c

xdata = np.array([1, 2, 3, 4])
ydata = np.array([3, 7, 13, 21])

popt, pcov = curve_fit(quad, ydata, xdata)
print(popt)
AMissing initial guess for parameters
BThe model function has wrong formula for quadratic
CThe independent and dependent variables are swapped in curve_fit call
DThe print statement is incorrect
Step-by-Step Solution
Solution:
  1. Step 1: Check curve_fit arguments

    curve_fit expects the model, xdata (independent), then ydata (dependent). Here, ydata and xdata are swapped.
  2. Step 2: Identify the error impact

    Swapping causes wrong fitting or runtime errors because the model expects x values first.
  3. Final Answer:

    The independent and dependent variables are swapped in curve_fit call -> Option C
  4. Quick Check:

    curve_fit(xdata, ydata) order matters [OK]
Quick Trick: Remember: curve_fit(model, xdata, ydata) [OK]
Common Mistakes:
  • Swapping xdata and ydata in curve_fit
  • Assuming model formula is incorrect
  • Thinking initial guess is always required

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