Why ensembles outperform single models in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple ensemble by averaging predictions from two models.

ML Python

ensemble_prediction = (model1.predict(X) [1] model2.predict(X)) / 2

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

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication instead of addition.

We add the predictions from both models to average them, which helps reduce errors.

2fill in blank

medium

Complete the code to calculate the accuracy of an ensemble model's predictions.

ML Python

accuracy = sum(ensemble_preds == y_true) [1] len(y_true)

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

A-

B*

C+

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or addition instead of division.

Accuracy is the number of correct predictions divided by total predictions.

3fill in blank

hard

Fix the error in the code to combine predictions from three models using majority voting.

ML Python

final_preds = (model1_preds + model2_preds + [1]) >= 2

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

Amodel3_preds

Bmodel3_pred

Cmodel_3_preds

Dmodel3

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names causing NameError.

The variable holding the third model's predictions is named model3_preds.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores model errors only if error is less than 0.1.

ML Python

errors = {model: error[1] for model, error in model_errors.items() if error [2] 0.1}

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A* 100

B>

C<

D+ 0.05

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators or forgetting to convert error to percentage.

The error is converted to percentage by multiplying by 100, and we keep errors less than 0.1.

5fill in blank

hard

Fill all three blanks to create a list comprehension that selects predictions from models with accuracy above 0.8.

ML Python

selected_preds = [preds for model, preds in all_model_preds.items() if accuracies[[1]] [2] [3]]

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

Amodel

B>

C0.8

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operator or wrong key for accuracies.

We check if the accuracy of each model is greater than 0.8 to select its predictions.