Linear regression with np.polyfit() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fit a linear model to x and y using np.polyfit.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])
coefficients = np.polyfit(x, y, [1])

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

A1

B3

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 2 or higher for a simple linear fit.

Using degree 0 which fits a constant only.

For a linear regression, the degree of the polynomial is 1.

2fill in blank

medium

Complete the code to predict y values using the fitted coefficients.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
coefficients = np.polyfit(x, np.array([2, 4, 5, 4, 5]), 1)
predicted_y = coefficients[[1]] * x + coefficients[1]

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A1

B0

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using coefficients[1] as slope.

Using negative indices incorrectly.

The first coefficient (index 0) is the slope (m) in y = mx + b.

3fill in blank

hard

Fix the error in the code to correctly fit a linear model and predict y.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])
coefficients = np.polyfit(x, y, [1])
predicted_y = coefficients[0] * x + coefficients[[2]]

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

A1

B0

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 0 or 2.

Using coefficients[0] as intercept.

Degree must be 1 for linear fit. Intercept is at index 1.

4fill in blank

hard

Fill both blanks to create a dictionary of x values and their predicted y values using np.polyfit.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])
coeffs = np.polyfit(x, y, [1])
predictions = {val: coeffs[0] * val + coeffs[[2]] for val in x}

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A1

B0

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong degree.

Mixing up slope and intercept indices.

Degree 1 for linear fit. Intercept is coefficients[1].

5fill in blank

hard

Fill all three blanks to create a list of predicted y values for x using np.polyfit and a list comprehension.

NumPy

import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])
coeffs = np.polyfit(x, y, [1])
predicted = [coeffs[[2]] * val + coeffs[[3]] for val in x]

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

A1

B0

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping slope and intercept indices.

Using wrong degree.

Degree 1 for linear fit. Slope is coeffs[0], intercept is coeffs[1].