Why fitting models to data reveals relationships in SciPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the curve fitting function from scipy.

SciPy

from scipy.optimize import [1]

Drag options to blanks, or click blank then click option'

Acurve_fit

Bfit_curve

Coptimize_curve

Dcurvefit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'curvefit' without underscore causes import error.

Trying 'fit_curve' which does not exist.

The function curve_fit from scipy.optimize is used to fit models to data.

2fill in blank

medium

Complete the code to define a linear model function for fitting.

SciPy

def linear_model(x, [1], b):
    return [1] * x + b

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Am

Ba

Cy

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'x' as a parameter name conflicts with input variable.

Using 'y' as a parameter name is confusing.

The parameter m represents the slope in the linear model m * x + b.

3fill in blank

hard

Fix the error in the code to fit the linear model to data arrays x_data and y_data.

SciPy

params, covariance = curve_fit([1], x_data, y_data)

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Afit_linear

Bcurve_fit

Clinear_model

Dmodel_linear

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'curve_fit' itself causes recursion error.

Using undefined function names causes NameError.

The first argument to curve_fit must be the model function, here linear_model.

4fill in blank

hard

Fill both blanks to create a dictionary of fitted parameters with keys 'slope' and 'intercept'.

SciPy

fitted_params = {'slope': params[1], 'intercept': params[2]

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A[0]

B[1]

C(0)

D(1)

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of square brackets causes TypeError.

Mixing up indices swaps slope and intercept.

The fitted parameters are returned as a tuple or array; index 0 is slope, index 1 is intercept.

5fill in blank

hard

Fill all three blanks to create a plot of data points and the fitted line using matplotlib.

SciPy

import matplotlib.pyplot as plt
plt.scatter(x_data, y_data, label='Data')
plt.plot(x_data, linear_model(x_data, [1], [2]), color='red', label='Fit')
plt.xlabel('[3]')
plt.ylabel('y')
plt.legend()
plt.show()

Drag options to blanks, or click blank then click option'

Aparams[0]

Bparams[1]

Cx

DX

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'params(0)' instead of 'params[0]' causes error.

Labeling x-axis with uppercase 'X' is less common.

Use the fitted slope and intercept from params to plot the line. Label the x-axis as 'x'.