Residual analysis in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate residuals from actual and predicted values.

ML Python

residuals = actual - [1]

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

Amedian

Bactual

Cmean

Dpredicted

Attempts:

3 left

2fill in blank

medium

Complete the code to plot residuals against predicted values using matplotlib.

ML Python

plt.scatter([1], residuals)
plt.axhline(y=0, color='r', linestyle='--')
plt.xlabel('Predicted Values')
plt.ylabel('Residuals')
plt.show()

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

Aactual

Bpredicted

Cresiduals

Dmean

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to compute residuals correctly.

ML Python

residuals = [1] - predicted

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

Aactual

Bmean

Cpredicted

Dmedian

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of residuals for each data point where residual is positive.

ML Python

positive_residuals = {i: residuals[i] for i in range(len(residuals)) if residuals[i] [1] 0 and residuals[i] [2] 0}

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

A>

B<

C==

D!=

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to compute mean squared error (MSE) from residuals.

ML Python

mse = sum([1]**2 for [2] in residuals) / [3]

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

Aresidual

Bresiduals

Clen(residuals)

Drange(len(residuals))

Attempts:

3 left