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Given the code below, what will be the output of print(popt)?

medium📝 Predict Output Q4 of 15
SciPy - Curve Fitting and Regression
Given the code below, what will be the output of print(popt)?
import numpy as np
from scipy.optimize import curve_fit

def linear_model(x, m, c):
    return m * x + c

xdata = np.array([0, 1, 2, 3, 4])
ydata = np.array([1, 3, 5, 7, 9])

popt, _ = curve_fit(linear_model, xdata, ydata, p0=[1, 0])
print(popt)
A[2. 1.]
B[1. 0.]
C[0.5 1.5]
D[3. 2.]
Step-by-Step Solution
Solution:
  1. Step 1: Analyze the data and model

    The data points follow the line y = 2x + 1 exactly.
  2. Step 2: Understand curve_fit output

    The optimized parameters popt will be close to [2, 1] for slope (m) and intercept (c).
  3. Final Answer:

    [2. 1.] -> Option A
  4. Quick Check:

    Fitted params = [2, 1] [OK]
Quick Trick: Check if data fits y=mx+c; params match slope and intercept [OK]
Common Mistakes:
  • Confusing parameter order
  • Ignoring initial guess effect
  • Misreading data pattern

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