Why ensembles outperform single models in ML Python - Quick Recap

Recall & Review

beginner

What is an ensemble in machine learning?

An ensemble is a group of multiple models combined to make better predictions than any single model alone.

beginner

Why do ensembles usually perform better than single models?

Because they combine different models, reducing errors from individual models and improving overall accuracy.

intermediate

How does averaging predictions in an ensemble help?

Averaging smooths out mistakes from individual models, so random errors cancel out, leading to more stable results.

intermediate

What is the role of diversity among models in an ensemble?

Diversity means models make different errors, so when combined, their mistakes don’t overlap much, improving accuracy.

beginner

Name two common types of ensemble methods.

Bagging (like Random Forest) and Boosting (like AdaBoost) are two popular ensemble methods.

What is the main benefit of using an ensemble of models?

A Improved prediction accuracy

B Faster training time

C Simpler model design

D Less data needed

Ensembles improve prediction accuracy by combining multiple models to reduce errors.

Which of these helps ensembles perform better?

A All models making the same errors

B Using only one model repeatedly

C Models being diverse and making different errors

D Ignoring model predictions

Diversity among models means their errors don’t overlap, so combining them improves accuracy.

What does bagging do in ensemble learning?

A Trains models on different random samples of data

B Trains one model multiple times

C Combines models by multiplying predictions

D Uses only the best model

Bagging trains models on different random subsets of data to create diverse models.

How does boosting improve ensemble performance?

A By training all models independently

B By training models sequentially focusing on previous errors

C By ignoring errors during training

D By using only one model

Boosting trains models one after another, each focusing on correcting errors from the previous model.

What happens when you average predictions from multiple models?

A Training time increases

B Errors increase

C Models become less diverse

D Random errors tend to cancel out

Averaging predictions reduces random errors, leading to more stable and accurate results.

Explain in your own words why ensembles usually give better results than a single model.

Think about how mistakes from different models can balance each other out.

Describe the difference between bagging and boosting in ensemble methods.

Consider how each method trains models and focuses on errors.