Classification evaluation (accuracy, precision, recall, F1) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate accuracy score using sklearn.

ML Python

from sklearn.metrics import [1]

accuracy = [1](y_true, y_pred)
print(f"Accuracy: {accuracy:.2f}")

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

Aaccuracy_score

Bprecision_score

Crecall_score

Df1_score

Attempts:

3 left

2fill in blank

medium

Complete the code to calculate precision score for binary classification.

ML Python

from sklearn.metrics import precision_score

precision = precision_score(y_true, y_pred, [1]=1)
print(f"Precision: {precision:.2f}")

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

Azero_division

Baverage

Clabels

Dpos_label

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to calculate recall score correctly.

ML Python

from sklearn.metrics import recall_score

recall = recall_score(y_true, y_pred, average=[1])
print(f"Recall: {recall:.2f}")

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

A'binary'

B'micro'

C'macro'

D'weighted'

Attempts:

3 left

4fill in blank

hard

Fill both blanks to calculate F1 score with macro averaging.

ML Python

from sklearn.metrics import f1_score

f1 = f1_score(y_true, y_pred, [1]=[2])
print(f"F1 Score: {f1:.2f}")

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

Aaverage

B'macro'

C'binary'

D'weighted'

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of classification metrics.

ML Python

metrics = {
    'accuracy': [1](y_true, y_pred),
    'precision': [2](y_true, y_pred, average='macro'),
    'recall': [3](y_true, y_pred, average='macro')
}
print(metrics)

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

Aaccuracy_score

Bprecision_score

Crecall_score

Df1_score

Attempts:

3 left