Confusion matrix in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that creates a confusion matrix.

ML Python

from sklearn.metrics import [1]

# Now you can use confusion_matrix() to evaluate predictions

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

Aclassification_report

Bconfusion_matrix

Caccuracy_score

Droc_auc_score

Attempts:

3 left

2fill in blank

medium

Complete the code to compute the confusion matrix for true and predicted labels.

ML Python

y_true = [0, 1, 0, 1]
y_pred = [0, 0, 0, 1]
cm = [1](y_true, y_pred)
print(cm)

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Aaccuracy_score

Bprecision_score

Croc_auc_score

Dconfusion_matrix

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to correctly compute and print the confusion matrix.

ML Python

from sklearn.metrics import confusion_matrix

y_true = [1, 0, 1, 0]
y_pred = [1, 1, 1, 0]
cm = confusion_matrix(y_true, [1])
print(cm)

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Aconfusion_matrix

By_true

Cy_pred

Daccuracy_score

Attempts:

3 left

4fill in blank

hard

Fill all three blanks to create a confusion matrix and extract true positives and false negatives.

ML Python

cm = [1](y_true, y_pred)
true_positives = cm[[2]][[2]]
false_negatives = cm[[2]][[3]]

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Aconfusion_matrix

B0

C1

Daccuracy_score

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to compute the confusion matrix and calculate accuracy manually.

ML Python

cm = [1](y_true, y_pred)
total = cm[[2]][[2]] + cm[[3]][[3]] + cm[[2]][[3]] + cm[[3]][[2]]
accuracy = (cm[[2]][[2]] + cm[[3]][[3]]) / total
print(f"Accuracy: {accuracy:.2f}")

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Aaccuracy_score

Bconfusion_matrix

C0

D1

Attempts:

3 left