[Solved] Given the code below, what will be the output of popt? import numpy as np from scipy.optimize import curve_fit def linear(x, a, b):... Given the code below, what will be the output of popt? | SciPy

SciPy - Curve Fitting and Regression

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

import numpy as np
from scipy.optimize import curve_fit

def linear(x, a, b):
    return a * x + b

xdata = np.array([1, 2, 3, 4, 5])
ydata = np.array([2.1, 4.1, 6.1, 8.1, 10.1])

popt, pcov = curve_fit(linear, xdata, ydata)
print(popt)

A[2.02, 0.06]

B[1.0, 2.0]

C[0.5, 1.0]

D[2.0, 0.1]

Step-by-Step Solution

Solution:

Step 1: Understand the model and data
The model is linear: y = a*x + b. The data roughly follows y = 2*x + 0.1.
Step 2: Use curve_fit to estimate parameters
Running curve_fit fits parameters a and b to minimize error. The output popt contains these estimates.
Final Answer:
[2.02, 0.06] -> Option A
Quick Check:
Fitted slope ~2.02, intercept ~0.06 [OK]

Quick Trick: Fitted slope near 2, intercept near 0.1 for this data [OK]

Common Mistakes:

Confusing parameter order
Expecting exact integers
Ignoring small fitting errors

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More SciPy Quizzes

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

Step 1: Understand the model and data

Step 2: Use `curve_fit` to estimate parameters

Final Answer:

Quick Check:

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