Why responsible ML prevents harm in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the main goal of responsible ML.

ML Python

print("The main goal of responsible ML is to prevent [1].")

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

Aharm

Baccuracy

Ccomplexity

Dspeed

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'accuracy' because it sounds important, but harm prevention is the key.

Responsible ML aims to prevent harm caused by biased or unfair models.

2fill in blank

medium

Complete the code to check if a model is accurate by comparing predictions.

ML Python

if all(model.predict(data) == [1]):
    print("Model is accurate")

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Alabels

Bdata

Cfeatures

Dbias

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'data' instead of 'labels' which is incorrect for comparison.

We compare model predictions to true labels to check accuracy.

3fill in blank

hard

Fix the error in the code that checks for bias in predictions.

ML Python

bias_detected = any(prediction [1] threshold for prediction in predictions)

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A<

B=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' or '==' which are assignment or equality, not comparison.

We check if any prediction is greater than a threshold to detect bias.

4fill in blank

hard

Fill both blanks to create a dictionary of features and their importance if importance is above 0.5.

ML Python

important_features = {feature: [1] for feature, importance in feature_importances.items() if importance [2] 0.5}

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Aimportance

Bfeature

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using feature as value or using '<' instead of '>'.

We keep features with importance greater than 0.5 and map feature to its importance.

5fill in blank

hard

Fill all three blanks to filter data points where age is above 18 and income is above 30000.

ML Python

filtered_data = [[1] for [2] in data if [3]['age'] > 18 and [3]['income'] > 30000]

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Apoint

Bx

Citem

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently or wrong variable for list element.

We iterate over data with variable 'point' and select 'point' for the list, using 'point' to access fields.