[Solved] What will be the output of this code?import numpy as np from scipy.optimize import curve_fit def model(x, a, b): return a * np.exp(b * x) xdata = np.array([0, 1, 2]) ydata = np.array([1, 2.7, 7.... | SciPy

SciPy - Curve Fitting and Regression

What will be the output of this code?

import numpy as np
from scipy.optimize import curve_fit

def model(x, a, b):
    return a * np.exp(b * x)

xdata = np.array([0, 1, 2])
ydata = np.array([1, 2.7, 7.4])
params, _ = curve_fit(model, xdata, ydata)
print(np.round(params, 2))

A[1. 1. 1.]

B[1. 1.]

C[1. 1. ]

D[0.5 0.5]

Step-by-Step Solution

Solution:

Step 1: Understand the model and data
The model is an exponential function with parameters a and b. Data roughly fits y = e^x.
Step 2: Use curve_fit to estimate parameters
Running curve_fit finds parameters close to a=1 and b=1 to fit the data.
Final Answer:
[1. 1.] -> Option B
Quick Check:
Estimated params ≈ [1, 1] [OK]

Quick Trick: curve_fit finds parameters fitting data best [OK]

Common Mistakes:

Expecting more parameters than defined
Misreading output shape
Ignoring rounding effects

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What will be the output of this code?

Step 1: Understand the model and data

Step 2: Use curve_fit to estimate parameters

Final Answer:

Quick Check:

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